Industry 4.0
Vol. 10 (2025), Issue 4

Table of Contents

  • TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

    • The Evolution, Current Impact and Future of Artificial Intelligence in Medicine

      pg(s) 122-127

      The application of artificial intelligence (AI) in medicine has emerged as a topic of global interest. Since the introduction of the term in the mid of 20th century, there has been considerable progress in the development of computer systems, leading into their integration into healthcare. At present, AI is increasingly being adopted across a growing number of medical specialties, where it contributes not only to diagnostic processes but also to the selection of appropriate treatments and the prediction of patient outcomes. AI prediction is particularly useful in the management of chronic conditions. Furthermore, AI demonstrates significant potential to expedite routine procedures, thereby allowing healthcare professionals to dedicate more time to cognitively demanding tasks, in which AI systems continue to present certain limitations. Nevertheless, despite notable advancements in recent years, several challenges must still be addressed in future research. These include the formulation of standards and guidelines for AI implementation, the assurance of cybersecurity to safeguard sensitive data, and the continuous education and training of healthcare practitioners. In conclusion, AI holds considerable promise for enhancing the quality and efficiency of healthcare delivery. Its role is not to replace human professionals, but rather to augment their performance and optimize the use of their time and expertise.

    • Engineering tool integration for complex system simulation and optimization

      pg(s) 128-132

      The integration of engineering support tools is essential for the efficient modeling, simulation, and optimization of complex technical systems. This paper presents a dynamic model of a micro-combined heat and power (mCHP) system, developed to validate the feasibility of integrating various computational environments. The approach leverages modular architectures, enabling seamless data exchange between distinct software platforms, thus supporting both detailed thermodynamic analysis and real-time performance optimization. The flexibility of this approach allows for the inclusion of diverse analytical frameworks, including neural network-based optimization, data-driven control strategies, and alternative programming languages, without being limited to a single computational tool. This adaptability makes the proposed architecture particularly suitable for evolving engineering applications, where rapid prototyping and iterativ e refinement are critical. The study highlights the potential of such integrated environments to enhance the design and operational efficiency of energy systems, providing a scalable foundation for future expansions.

  • DOMINANT TECHNOLOGIES IN “INDUSTRY 4.0”

    • Design and study of the operation of a green waste composter with energy cogeneration

      pg(s) 132-135

      Nowadays, renewable bioenergy is considered as one of the ways to provide energy at the expense of fossil fuels. The possibility of obtaining energy from organic materials through bio-electrochemical systems attracts considerable interest. Green waste is organic plantbased materials generated from grass clippings, broken branches, fallen trees and others, the utilization of which is currently not very effective. In the present study, we constructed a composter based on bio-electrochemical systems for the utilization of green waste with cogeneration of energy. The composter showed good performance in terms of both the decomposition of organics and the generated energy.

    • A review on the technological specificity of the synthesis of quartz ceramics

      pg(s) 136-139

      A survey of the prevailing trends in the field of quartz ceramics has been carried out. The article examines the main technological stages characteristic of the synthesis of quartz ceramics and the production of various product categories. The main technological stages characteristic of the synthesis of quartz ceramics and the production of various categories of products have been presented. The technological conditions applicable to the processing of raw materials, the preparation of suspensions with optimal characteristics, the molding of blanks and the heat treatment of semi-finished products have been examined. The role and influence of various technological factors in the formation of the structural characteristics and operational indicators of the products have been analyzed.

  • BUSINESS & “INDUSTRY 4.0”

    • Current trends in HRM in the era of Industry 4.0: challenges and opportunities of using artificial intelligence

      pg(s) 140-144

      The article provides an insight into the analysis of trends in contemporary HR in the era of Industry 4.0, with an emphasis on the role of artificial intelligence (AI) in managers’ decision-making processes. The study addresses the opportunities and threats associated with the implementation of AI within HR and its impact on organizational effectiveness and human resource management. Using secondary data analysis and literature synthesis, we offer a deeper insight into modern HR management approaches and discuss the potential risks associated with digital transformation. The aim of the study is to provide practical recommendations for managers on how to deal with the challenges of the current Industry 4.0 era.

  • SOCIETY & ”INDUSTRY 4.0”

    • Critical assessment of some applications of artificial intelligence

      pg(s) 145-147

      Artificial intelligence applications are rapidly entering many aspects of business, state administration and education with the aim of increasing productivity and efficiency. The report examines the possibilities for implementing artificial intelligence in their activities and the related ethical and legal aspects, as well as the risks. A review and analysis of some applications of AI is made.

    • Digital twins in security

      pg(s) 148-158

      Digital twins combine real-time data from physical objects with advanced simulation techniques to enhance the security ecosystem. They enable predictive analytics, anomaly detection, and optimized decision-making in complex operational environments by integrating technologies such as IoT, artificial intelligence, and blockchain.

    • Digitalized Work Environment in Industry 5.0: Generational Specificities and Prerequisites for Personalized Motivational Approaches

      pg(s) 153-156

      The present article analyzes intergenerational differences in values, attitudes and motivational preferences of employees in the context of the digitalized work environment of Industry 5.0. Five generations are examined – from Baby Boomers to Generation Alpha – through their chronological, social and behavioral profiles. Summary tables are presented with leading motivators, work styles and expectations towards organizations. The paper formulates practice-oriented recommendations for personalized managerial and motivational approaches aligned with the characteristics of each generation and human-centered model of Industry 5.0. The conclusions of the study indicate that strategic consideration of generational differences is essential for building effective, inclusive and adaptive human resource practices in line with the goals of Industry 5.0.

    • Using semantic kernel with openai for agentic ai solutions for autonomous environmental control in smart homes

      pg(s) 157-160

      The integration of the Semantic Kernel with OpenAI presents a novel framework for developing agentic artificial intelligence (AI) solutions for autonomous environmental control in smart homes. This approach leverages Semantic Kernel’s capabilities in natural language understanding, contextual reasoning, and task orchestration, combined with OpenAI’s advanced generative AI models. Together, these technologies enable the creation of intelligent agents capable of interpreting complex user commands, understanding contextual nuances, and autonomously managing dynamic environmental conditions within smart home ecosystems.
      The solution meets the challenges of making real-time decisions and providing personalized user experiences in smart homes. Semantic Kernel allows the design of flexible AI agents that manage memory, interact with external APIs, and execute tasks efficiently. When combined with OpenAI, these agents acquire superior language processing and conversational skills, facilitating seamless interactions with users and other smart home devices. This leads to a smart home ecosystem where AI optimizes lighting, temperature, quality, and energy use based on user preferences and context.
      A significant feature of this framework is its capacity to function autonomously while adjusting to user input and evolving scenarios. Semantic Kernel’s contextual memory facilitates tailored interactions by retaining user preferences and previous actions, enabling AI agents to modify their responses accordingly. For instance, the system can learn and adapt to a user’s preferred lighting and temperature settings throughout the day or respond to changes in weather or energy availability.
      The framework includes design principles and implementation strategies for integrating Semantic Kernel with OpenAI in smart home environments. Practical examples show how these technologies can turn traditional smart homes into fully autonomous systems. Demonstrations cover scenarios like coordinating multiple devices for energy efficiency, adapting environmental control based on user activity, and AI-driven emergency responses for safety.