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Innovation processes are strongly in uenced by changes in economic, political, technological and other external factors. For instance, economic instability and political uncertainty can both stimulate and limit innovative activity in organisations. Transmodern innovation is a concept that involves scienti c and technological advancements that may remain unutilised until favourable changes occur in technological or economic conditions. The purpose of this study is to develop a conceptual model for transmodern innovation that takes into account the dynamics of innovation, including the intensity, economic prerequisites, external changes and degree of innovation adaptation. This model will help organisations to better understand and respond to the complexities of the innovation process. The resulting model is a comprehensive tool for analysing changes in innovation activity and the external environment over di erent time phases, including the initial state (t0), the transition to new conditions (t1) and the nal state (tx). In this model, the ‘Final stage of tx’ block represents the nal stage, which allows us to draw conclusions about the success of adaptation and innovation development. This is the basis for formulating strategic conclusions and recommendations for future development.
This article analyses the sustainability of China’s economic growth in light of global challenges, focusing on macroeconomic changes in recent decades and their impact on the country’s economy. The study covers the period 1962-2022 and uses data from various sources, including the World Bank, International Monetary Fund, Organisation for Economic Cooperation and Development, and national statistical data from the People’s Republic of China. Correlation analysis methods are used to assess the impact of socio-economic indicators on economic growth, revealing signi cant correlations between gross domestic product and various indicators such as external debt, urbanisation, technological development, and the standard of living. The main conclusion of the analysis is that economic diversi cation and investment in high-tech industries are crucial for maintaining sustainable growth in China. The ndings indicate the need for future research assessing the potential for reducing the environmental impact of industrialisation and improving social policies in a changing global economy.
This article explores the integration of digital solutions to enhance the sustainable development of agribusiness through the activation of the introduction of intellectual capital. The analysis is carried out taking into account various factors affecting yields, such as soil type, fertilizer use, market prices, employee education level, product demand, and automation level. The level of automation, the use of geographic information systems, access to big data, and hours of employee training were chosen as factors of intellectualization. Random forest, ARIMA, SARIMA, and LSTM models were used to predict yields. The data were taken from the statistical portals of Armenia and Georgia (137 observations). The results of the study show that the LSTM model demonstrated the best prediction accuracy with an average absolute error of 8.30 and a standard error of 102.47. The random forest model showed an average absolute error of 24.87 and a standard error of 828.23, while the ARIMA and SARIMA models did not show significant results. The study revealed significant correlations between digital solutions characterizing the level of intellectual capital in agricultural enterprises and agricultural land productivity, including the level of automation and access to big data. Analysis was also conducted on the impact of intellectual capital on the sustainability of agribusiness, including the impact of the level of education and training hours of employees. It is concluded that the integration of innovative technologies, such as big data and automation, contributes to improving the efficiency of agricultural production.
This article analyses the sustainability of the agro-industrial complex (AIC) in the Eurasian Economic Union (EAEU) countries with an emphasis on food security. The study covers challenges and threats to food security in Russia, Belarus, Armenia, Kazakhstan, and Kyrgyzstan, given the difficult geopolitical situation. The article examines data from the national statistical services of the EAEU countries, as well as international sources such as the FAO and the World Bank. Correlation and cluster analysis approaches are applied to assess the impact of socioeconomic indicators on the sustainability of the AIC. Significant correlations between indicators of food security and such factors as the volume of agricultural production, investments in the agricultural sector, the level of technological development, and government support are revealed. On average, for the period from 2015 to 2022, the added value of agriculture amounted to 8.2% of GDP, and the food production index was 104.1. The results of the cluster analysis showed that the EAEU countries can be grouped by levels of agricultural development and food security. Thus, K-means and GMM identified three clusters in which Russia found itself both in a separate cluster and in combination with other countries. Agglomerative and spectral clustering also showed similar results, distinguishing three main groups of countries. The average silhouette coefficient for agglomerative and spectral clustering was 0.41, which indicates a better clustering quality compared to K-means and GMM (0.38). It is confirmed that integration and coordination of efforts within the EAEU, as well as diversification of agricultural production and increased investment in innovation, determine the state of sustainability of the agro-industrial complex.
The contemporary information landscape is characterised by a huge amount of data available for analysis using a variety of research tools and methods. Considering the limitations of using individual models and methods, it is worth employing an approach that combines functional and logical autoregression methods to conduct a more accurate analysis of trends and topics in the information space. Considering this context, this work aims to develop an algorithm to identify and analyse topics that would be relevant in the future using autoregression methods. The process begins with the quantification and normalisation of data, which significantly affect the quality of analysis. The main focus of this study is to implement the autoregression method to analyse long-term trends and predict future developments in the selected data. The proposed algorithm evaluates the forecast of these future developments and analyses graphical trends, thus conducting a more detailed study and modelling of future data dynamics. The regression coefficient is used as a quality criterion. The algorithm concludes with a polynomial function to help identify topics that will be relevant in the future. Overall, the proposed algorithm can be considered an effective tool for analysing and predicting future trends based on the analysis of historical data, thus contributing to the identification of prospects for technological development.
The present paper develops an invariant ontology of strategic interaction in a sociotechnical system using game theory tools. In the course of the research, ontologies are considered tools for modelling sociotechnical systems, including tools for social and technical process integration. The demand for these tools derives from the need to integrate people into technical systems as equivalent and equal elements that exert both external and internal influence on the system. Such sociotechnical models have already been applied to describe enterprise information structures, but they lack a description of decision-making between the system elements within the strategic inter-action. As part of the solution to this problem, an ontology-based model of a sociotechnical system describing the interaction of both social and technical elements through game interaction is developed. Each of the participants in the interaction is described in terms of game theory, with the allocation of possible strategies and the corre-sponding winnings. Through the interactive entities within the game theory model, game interaction takes place between the participant and appropriate behaviour strategy selection. The model is a exible, scalable tool for building simulation models of sociotechnical systems. The results obtained will be tested when real sociotechnical systems are built, and the ontology will be re ned according to the results obtained.
Today, in order to develop national economies, it is necessary to pay attention to the economies of the regions of the countries. The regional economy is the basis of the national welfare. In the Russian Federation there are a number of problems with regional development, one of which is the differentiated development of the constituent entities of the Russian Federation. Every year, the difference in development increases due to the rapid growth of large economic regions and the lag in regions removed from the central regions. In the context of such key competition factors, this development becomes competitive in the region. The article examines various concepts of and approaches to regional competitiveness that contribute to the formation of comprehensive economic cooperation. Through analysis of statistics on indicators of the socio-economic development of some regions of the Russian Federation, the reason for the observed disproportion was identified. This reason is the outflow of population. A person with strength and skills is a key part of building a regional economic system. To solve problems that arise in the work, a model for increasing regional competitiveness is proposed, which includes three stages. The first is basic, focusing on building the foundations of production; the second stage involves focusing on the development of science and education; and the third is creative, focused on the development of creative industries. Thus, the model proposed by the author meets the criterion of complexity, as well as the requirements for increasing the competitiveness of regions in the medium and long term, which will contribute to the socio-economic development of the constituent entities of the Russian Federation, balance and balance in the level of development of the country as a whole.
This article considers entropy analysis as a tool for assessing the sustainability and integration of regional economies into national and international economic systems. Three key types of entropy are de ned - economic diversi cation, income and employment distribution and interregional ties. The methodology for calculating entropy indicators based on the generalised Shannon entropy formula is presented. A comparative analysis of three hypothetical regions was conducted on the basis of entropy indices. The obtained results allow us to quantitatively assess the speci cs of regional development, identify imbalances and propose strategies to improve the sustainability and economic diversi cation of regions.
This article focuses on improving the information security of industrial enterprises through the automation of data transmission processes. As a solution, an autonomous unmanned aerial vehicle (UAV) equipped with three microcontrollers is proposed to handle ight control, data processing and transmission and information protection. The system utilises infrared data transmission channels, hardware encryption and a mechanism for the physical destruction of the storage medium, ensuring a high level of protection against cyberattacks and data breaches. The drone’s architecture is isolated from corporate networks and features mobility and autonomy, making it e ective in environments with limited infrastructure. The modular design of the device allows for adaptation to various application scenarios. The research results demonstrate that the proposed solution provides reliable and secure data transmission, enhancing the resilience of enterprises to modern cyber threats.
This paper describes the development of an agile project management system. This research is relevant because of the feature of agile projects consisting of changing requirements. We developed the architecture of the backlog and tasks management system, including a duration prediction module. Using the developed system, managers have the opportunity to divide project processes into sprints depending on the prediction and value for the stakeholders. The authors propose a method for predicting agile project duration by formalising the application of expert Story Point evaluation and subsequent Monte Carlo simulation. Based on the developed method, an algorithm for the dynamic prediction of task duration was designed. Integration of the developed method into the system made it possible to reduce the assumptions and limitations associated with updating user story data and participation in the projects of new and cross-functional teams. The developed system allows managers to cope with agile project bottlenecks.