The research focuses on analyzing the performance of industrial enterprises as the main value-creating area in sustainable cities from a microeconomic perspective. In the context of Azerbaijan, the microeconomic aspects of Baku’s industrial development and the sustainability of economic growth are studied using real statistical indicators. Since the coordinated direction of social, economic, political, and environmental processes to address pressing issues requires identifying the microeconomic aspects of sustainable development, this task is set as the article’s goal. The dynamic analysis of relevant statistical indicators shows that innovation, digitalization, and improvements in the financial conditions of industrial microeconomic entities in sustainable cities lay the foundation for a competitive basis for sustainable development. It should be noted that the relevance of the article is the determination of the characteristics of the activities of all process participants, especially microeconomic entities, within the framework of sustainability goals, given the intensiveness of settlement, transport-logistics and industrial production processes in the city chosen as the object of research, and the formation of a mechanism for influencing them. The study of the industrial development of sustainable cities at the microeconomic level in the article is important for optimizing risks and achieving benefits for economic entities in the context of realizing complex goals. Here, in particular, the relevant suggestions and recommendations are reflected to improve the efficiency of microeconomic aspects of industrial production and services toward the goals of sustainability in the city of Baku.
As it is clearly known that advances of artificial intelligence has had significant impact on different fields including translation sector in recent years. The paper starts with the evolution of artificial intelligence from rule-based systems to sophisticated machine translation and AI-integrated tools including natural language processing and computer-assisted translation platforms, and analyzes the pivotal influence of AI on the translation sector. It is aimed to investigate the advantages of AI and use of its applications in different fields. The research shows that no matter how much artificial intelligence develops, human translation remains relevant, and it is still not possible to achieve a translation as accurate as the human brain. There is a need of human oversight and post-editing in AI translation. It ends with suggestions for future perspectives of using machine and human translations.
In the dynamic e-commerce environment, customer-centric strategies have become essential for achieving sustainable growth. This study explores the influence of personalization and discount strategies on revenue and customer loyalty. Utilizing econometric models and EViews software, the research analyzes multi-year data from diverse e-commerce platforms to assess the quantitative impact of these strategies. Regression and panel data models are employed to uncover relationships between personalization, discount rates, and key performance metrics such as revenue, mobile shopping trends, and loyalty program participation. By integrating insights from prior literature and presenting robust empirical findings, this study provides actionable recommendations for optimizing customer-oriented management in e-commerce, ultimately contributing to a deeper understanding of effective strategies in the digital marketplace.
Urban energy efficiency is a critical challenge due to increasing population density and energy demands. Integrating Internet of Things (IoT) technologies with fuzzy logic presents a promising approach to enhancing energy efficiency in urban areas. This study proposes a Fuzzy Logic-Driven Energy Efficiency model that utilizes IoT data to optimize energy consumption. The model takes four input variables: energy consumption (kWh), occupancy level (
