International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This study examines the effects of light-emitting diode (LED) lanterns and partition colors on the psychological condition of individuals in evacuation shelters. A previous questionnaire survey involving 193 participants evaluated emotional responses to four LED colors (yellow, green, blue, and red). Yellow light was most frequently perceived as calming and comfortable, whereas red light was strongly associated with tension and anxiety. Building on these emotional findings, we investigated environmental factors through an experiment involving blue, pink, and green partitions. The participants stayed inside each partition for 25 min, and their vital signs, including body temperature, pulse rate, and blood pressure, were measured every 5 min. The results indicate that blue partitions produced the most stable physiological responses, including lower systolic blood pressure and steady pulse rates, reflecting parasympathetic nervous system activation. In contrast, the pink and green partitions increased pulse rates and led to greater fluctuations, indicating higher stress. These findings demonstrate that appropriate lighting and partition colors can promote psychological stability and improve shelter environments. This study also highlights the importance of simple, low-cost environmental design strategies for stress reduction during prolonged evacuations.

The rapid evolution of Industry 4.0 emphasizes not only automation and connectivity, but also the seamless integration of humancentric cyber-physical systems (CPS). This paper presents a secure, interoperable wireless communication framework based on the emerging Matter and Thread protocols, designed for real-time, local, and privacy-aware interactions between people and machines. While traditional wireless IoT protocols such as Zigbee and Bluetooth face challenges with fragmentation, lack of interoperability, and limited security models, Matter and Thread offer a unified, IP-based mesh network that is vendor-neutral, scalable, and designed with end-to-end encryption and device attestation by default. We apply this architecture to a practical use case in assistive IoT: enabling users with disabilities to interact with smart environments through secure, low-power wireless devices. This includes wearable panic buttons, smart locks, and environmental sensors that respond in real time, even in cloud-disconnected conditions, which is a critical requirement for safety in CPS. The system also supports multi-platform integration (Android, iOS, embedded Linux) without duplicating infrastructure. The paper presents a modular threat model, performance analysis of secure onboarding and encrypted control flows, and discusses deployment challenges in real-world industrial or healthcare automation contexts. This approach demonstrates how inclusive, secure-by-design wireless CPS can be realized with current technologies, contributing to more resilient and human-aware Industry 4.0 systems.

The main sources of green energy (GE), considered as the cleanest forms of energy or types of renewable energy sources (RES), are wind, water, sun, and earth. While the world, particularly in the most developed countries, has made significant progress in adopting and applying the various forms of green energy (GE), in Georgia this field is in its initial stage and it is not possible to predict when the first major positive developments will be made. This paper presents an analysis of total energy supply (TES) and domestic energy production (DEP) in Georgia for the period 2000-2023. The value of the continuous annual growth rate (CAGR) in Georgia for this period for TES is increased by 3.10 [%] and for DEP is decreased by -0,21 [%] and cumulative growth index (CGI) for TES is increased by 201.63 [%] and for DEP is decreased by 95,34 [%].

The transition toward sustainable, smart, and automated water treatment technologies represents a key goal of the Industry 4.0 paradigm. This study presents the development and evaluation of magnetic Mg–Fe oxide spinel nanoparticles as efficient and recyclable sorbent materials for arsenic removal from real municipal groundwater containing naturally elevated concentrations. The materials were synthesized via microwave-assisted co-precipitation, structurally confirmed to possess a spinel-type crystal structure, and characterized magnetically using SQUID magnetometry. Batch adsorption experiments using groundwater from Zrenjanin (Serbia) demonstrated rapid and highly efficient arsenic removal. The most effective composition (sample S7, n(Mg)/n(Fe) = 0.213) reduced arsenic concentrations below the WHO limit of 10 μg/L within two minutes of contact time, achieving a maximum sorption capacity of 407 μg/g. Kinetic modeling showed an excellent fit to the pseudo-second-order model (R2 = 0.9999), indicating a chemisorption-controlled mechanism. The obtained results confirm that Mg–Fe oxide spinels combine high sorption efficiency, magnetic separability, and rapid kinetics, making them suitable for integration into sensor-controlled, modular, and automated water treatment systems. The study establishes a technological basis for the Industry 4.0-aligned development of smart magnetic sorbents for sustainable drinking water purification.

The report examines the current state and challenges of vocational education in the transport and energy sectors. It analyzes the need for modernization through project-based learning, digital transformation, and team-based learning, which have been proven to enhance outcomes and prepare learners for Industry 4.0. The report presents international practices in dual education and effective models of partnerships between education and industry. Finally, it outlines the main barriers—institutional, resource-related, social, and those stemming from resistance to change—that hinder the widespread implementation of innovative approaches.

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.

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.

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.

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.

Cybersecurity studies increasingly prioritize empirical methodologies to understand and alleviate security risks arising from human behavior, organizational practices, and the advancement of technologies used to perform cyberattacks. This research paper explores how the school management and key educators in Bulgarian K-12 schools comprehend cybersecurity within the academic framework of the Bulgarian education system. Our survey of 927 Bulgarian K–12 educational institutions revealed that a very small number of them assess their level of cybersecurity as extremely low. Respondents see cybersecurity training as essential for ensuring information security within their institutions. Our findings indicate that only 25% of K-12 employees are able to incorporate this training into their qualification programs.

Information and communication technologies (ICT) are a set of equipment (devices, systems, etc.) and techniques with associated software (software systems and software tools) that have been developed and used for more efficient communication, creation, distribution, storage and management of information. The IDI (ICT Development Index) index is most often used to measure ICT resources, which is defined as a composite index that combines 11 indicators (classified into 3 sub-indices: Access, Use and Skills). Data on values of IDI index has been retrieved from Measuring the Information Society Report (MISR), which since 2008 has been publishing by International Telecommunications Union (ITU). The IDI index for 2017 was determined for 176 countries of the world, with the first on the ranking list was Iceland with IDI2017=8.98 and the last Eritrea with IDI2017=0.96. The mean value of IDI index for 2017 was Avg=5.11, standard deviation was StD=2.22 and coefficient of variation was CoV=43.52. In this paper is given of analysis of IDI (ICT development index) for measuring ICT resources in Georgia. The data were approximated using a 3rd degree polynomial regression model (PRM3) with a correlation coefficient R=0.9964, determination coefficient R^2=0.9928 and adjustment coefficient of determination is AdjR^2=0.9885.

Logistics represents an industrial and multidisciplinary scientific field that deals with the analysis, planning, management, and control of all logistics operations (distribution, warehouse, shipping, transportation etc.). Modern trends in logistics involve the use of advanced software systems, tools and digital technologies (DT) to address all operations and activities within a single logistics task. Digital technologies (DT) in logistics encompass the application of intelligent and smart digital technologies, smart technologies (ST), smart manufacturing (SM), smart logistics (SL), artificial intelligence (AI), cloud computing (CC), data mining (DM), and more. The application of all these technologies in logistics has created a new concept called Logistics 4.0, which is based on the concept of Industry 4.0. While SMEs (small and medium-sized enterprises) and corporations in middle-developed and underdeveloped countries of the world are beginning to think about introducing the concept of Logistics 4.9, a new concept of Logistics 5.0 has already been created and defined. Within the Logistics 4.0 concept, which is part of Industry 4.0, advanced and modern versions of software systems and tools in the field of logistics are used for planning, transportation, warehousing, distribution and other logistics operations. Modern software systems and tools for logistics are classified into several groups: logistics database (LDB) for quick search and access of logistics data, logistics information systems (LIS) for storing, processing and management of logistics data and information, logistics management systems (LMS) for managing logical operations, logistics decision support systems (LDSS) for decision-making in specific logistics operations, logistics expert systems (LES) and other software systems and tools for logistics operations. This paper provides an overview of different software systems and tools for logistics operations.