Industry 4.0
Vol. 11 (2026), Issue 2

Table of Contents

  • TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

    • Information Theory for Medical Data Fusion

      pg(s) 46-50

      Data fusion is the process of integrating multiple heterogeneous data sources to produce more accurate, comprehensive, and useful information than any single source alone. For the medical data fusion, this information may come from imaging, genomic data, clinical records, and physiological signals. It has emerged as a cornerstone of modern precision medicine. Information theory provides a rigorous mathematical framework for quantifying uncertainty, measuring information gain, and can thus be used to optimize fusion strategies across diverse clinical contexts. This paper presents a review of information-theoretic approaches suitable for medical data fusion, covering: fundamental concepts (entropy, mutual information, Kullback-Leibler divergence, rate-distortion), theoretical frameworks (information bottleneck, transfer entropy, partial information decomposition), and their application across major clinical domains. We synthesize recent advances in fusion-based architectures, discuss the critical challenges of uncertainty quantification, and provide practical guidelines for implementing information-theoretic fusion in clinical settings. Through a systematic analysis, we identify key challenges, including parameter sensitivity, missing modalities, and clinical interpretability, to outline promising directions for future research. This review aims to provide clinicians, researchers, and developers with a comprehensive understanding of how information theory can transform medical data for improved diagnostic accuracy, prognostic precision, and personalized patient care.

    • Artificial intelligence approaches for modeling nonlinear dynamical systems

      pg(s) 51-57

      Nonlinear dynamical systems arise in numerous scientific and engineering domains, including physics, economics, biology, and control theory. Their complex behavior, sensitivity to initial conditions, and possible chaotic dynamics make accurate modeling and prediction challenging using traditional analytical approaches alone. In recent years, artificial intelligence (AI) techniques have demonstrated strong potential for modeling nonlinear and complex systems through data-driven methods. This paper explores artificial intelligence approaches for modeling nonlinear dynamical systems, focusing on the integration of machine learning techniques with classical mathematical modeling. We consider representative nonlinear systems and analyze how neural networks, regression models, and hybrid AI–mathematical frameworks can be used to approximate system behavior, predict future states, and capture hidden structures in time-series data. Special attention is given to systems exhibiting chaotic behavior, where small perturbations in initial conditions can lead to significant divergence in trajectories. The study presents numerical simulations and comparative analyses between traditional mathematical models and AI-based approaches. The results highlight the advantages of machine learning methods in capturing nonlinear patterns and improving predictive accuracy, especially when analytical solutions are difficult or unavailable. Additionally, we discuss the interpretability of AI models in the context of dynamical systems and outline potential applications in engineering, intelligent control, and data-driven system identification. The proposed framework contributes to the growing intersection between dynamical systems theory and artificial intelligence by demonstrating how AI tools can support the analysis and modeling of complex nonlinear phenomena. This work aims to provide a foundation for future research on hybrid mathematical–AI methods for understanding and predicting complex systems.

    • Imagined pedestrian encounters: analyzing world-model rollouts in a reinforcement learning driving agent

      pg(s) 58-62

      World-model reinforcement learning agents plan by imagining future trajectories through a learned latent dynamics model. In safety-critical driving tasks, the quality of these imagined rollouts — particularly their representation of vulnerable road users — directly determines whether the agent can anticipate and avoid dangerous situations. We analyze the imagined rollouts of a DreamerV3 agent trained to navigate urban environments in CARLA using semantic segmentation and depth observations. By decoding the agent’s latent imagination into observation space, we qualitatively and quantitatively examine how pedestrians are represented in imagined futures, comparing episodes that result in collision against those where the agent successfully avoids. We investigate whether the world model accurately predicts pedestrian presence and motion in its imagined horizon, and whether reconstruction fidelity of pedestrian regions correlates with avoidance outcomes. Our analysis provides insight into the internal representations that underlie emergent safety behaviors in model-based driving agents and highlights limitations of finite imagination horizons for pedestrian safety.

    • APPLICATION OF ARTIFICIAL INTELLIGENCE IN PRE-UNIVERSITY EDUCATION – CASE STUDY SCHOOLS IN THE MUNICIPALITY OF KAMENICA – KOSOVO

      pg(s) 63-68

      Pre-university education in general is facing various challenges, but in the schools of the Municipality of Kamenica the challenges are even greater in improving learning outcomes in teaching and learning, these challenges range from identifying individual student needs and optimizing learning resources.
      This paper examines the role and impact of artificial intelligence (AI) in improving and facilitating learning circumstances in schools in the Municipality of Kamenica in Kosovo. The main goal is to analyze how AI solutions can improve the level of learning, support effective teaching and help in assessing and attracting students by increasing their level of concentration.
      In this paper, we have taken an empirical approach using real data from several pre-university education schools in Kamenica, which include test scores, attendance statistics, and information on learning activities. With the help of machine learning techniques, models have been built that identify student profiles with different performance and recommend personalized learning strategies for each profile. An AIbased recommender system has also been developed that suggests teaching materials and relevant exercises using virtual laboratories according to the needs and individual progress of students.
      The results show that the use of AI tools helps in identifying student weaknesses more quickly, in creating personalized lesson plans and in facilitating the work of teachers for continuous monitoring and evaluation. The analysis also shows the perception of teachers and parents across the Municipality of Kamenica towards the integration of AI in teaching practice, highlighting the challenges and opportunities for wider implementation in pre-university education in Kosovo.
      This case study that we have conducted in the Municipality of Kamenica offers a practical and strategic contribution to municipal education policies towards the effective use of advanced technologies in the teaching process not only in the Municipality of Kamenica but also beyond.

  • DOMINANT TECHNOLOGIES IN “INDUSTRY 4.0”

    • Preparation and Optical Properties of Gelatin-SiO2 Hybrid Coatings on Glasses for Photovoltaic Applications

      pg(s) 69-71

      In this study, gelatin-SiO2 hybrid coatings were prepared on glass substrates using an acid-catalyzed sol-gel method. The deposited layers were subjected to thermal treatment at 50°C, 150°C, and 250°C to evaluate the effect of temperature on their structure and optical properties. Spectroscopic investigations in the UV-VIS-NIR range show that the hybrid coatings enhance optical transmittance and reduce reflective losses compared to uncoated glass. The best optical performance was observed for the sample thermally treated at 150 °C (GS 150), where an optimal balance between the organic (gelatin) and inorganic (SiO2) phases was achieved, resulting in maximum transmittance and minimal light reflection. At lower treatment temperatures (50°C), the coating exhibited higher porosity but lower structural stability, whereas at higher temperatures (250°C), partial thermal degradation of the gelatin phase led to layer densification and a slight reduction in the antireflective effect. These results demonstrate that gelatin can be successfully used as a biopolymer component to control the microstructure and optical properties of sol-gel hybrid coatings. Improving optical transmittance and reducing reflective losses of glass surfaces is crucial for enhancing photovoltaic module efficiency, as it allows a greater amount of solar radiation to reach the active layer of solar cells.

  • BUSINESS & “INDUSTRY 4.0”

  • SOCIETY & ”INDUSTRY 4.0”

    • AI ethics education as a tool to minimize risks in medicine

      pg(s) 83-86

      The rapid integration of artificial intelligence (AI) into medical practice has created unprecedented opportunities for clinical decision support, diagnostics, and patient communication. At the same time, it has introduced new categories of risk, including hallucinated outputs, biased recommendations, privacy breaches, and overreliance by inexperienced practitioners. These risks are amplified in high‑ stakes environments such as medicine, where erroneous AI‑ generated information can directly impact patient safety. This paper argues that robust AI ethics education is an essential risks mitigation strategy for future healthcare professionals. Drawing on the results of a survey conducted among medical students, the study examines perceptions of AI use in medicine, the perceived importance of AI ethics education, and expectations regarding its content. Respondents showed clear optimism about the role of generative artificial intelligence (GenAI) in medicine, with strong majorities agreeing that it can positively change clinical practice, improve patient care, and find useful applications. At the same time, the high number of undecided responses (especially regarding patient‑ care quality) reflects ongoing uncertainty about reliability and safety. Students also strongly emphasized the need for structured AI ethics education, as most agreed that it raises awareness of ethical issues and should involve multidisciplinary expertise. Across all proposed topics, including data privacy, bias, explainability, safety, fairness, and autonomy, students rated the relevance of ethics-related content as high, indicating a clear expectation that medical curricula must prepare them to navigate the respective risks and limitations. By synthesizing these findings with current debates on AI governance and medical safety, the paper positions ethics‑ driven education as a foundational component of responsible AI deployment in healthcare. The paper argues for embedding AI ethics directly into medical curricula as a proactive risk‑ mitigation tool. The contribution of this work lies in demonstrating, through empirical evidence, that future clinicians recognize both the promise and the
      dangers of AI in medicine.

    • AI and digital ethics in the age of generative systems-principles, standards and accountability across cultures

      pg(s) 87-89

      Generative AI intensifies ethical risks around opacity, bias, responsibility gaps, and cross-cultural legitimacy. This paper synthesizes contemporary literature and proposes a layered governance model with three layers that translates universal ethical principles into culturally adaptive and sector-specific controls. The contribution is a practical pathway from principles to standards, enabling transparency, accountability, and contestability across the AI lifecycle.

    • Legal regulation problems in transport logistics: challenges and prospects

      pg(s) 90-92

      Transport logistics plays a decisive role in supporting global trade and economic development. The effectiveness of logistics systems largely depends on the quality, coherence, and adaptability of their legal regulation. In recent decades, profound changes in supply chains, technological innovation, and sustainability requirements have revealed significant shortcomings in existing legal frameworks governing transport logistics. This article analyzes the key problems of legal regulation in transport logistics, including fragmentation of legal regimes, challenges of multimodal transport, contractual and liability issues, digitalization, environmental regulation, and enforcement mechanisms. Using doctrinal legal analysis and comparative perspectives, the study identifies systemic weaknesses and proposes directions for regulatory improvement. The findings contribute to ongoing academic and policy debates on the modernization and harmonization of transport logistics law.

    • Integrating ai into insolvency procedures, challenges and opportunities in Albania

      pg(s) 93-95

      Bankruptcy procedures are complex and often slow, requiring extensive documentation, strict deadlines, multiple creditors, significant human resources, and numerous judicial decisions In Albania, these procedures remain predominantly manual; therefore, the use of Artificial Intelligence opens new opportunities to enhance efficiency and transparency in these procedures. The main benefits include reducing the duration and improving the effectiveness of actions, increasing the accuracy and reliability of the process, and generating useful statistics for the formulation of public policies. Nevertheless, challenges remain related to the adoption of an appropriate legal framework, the protection of sensitive data, the transparency of algorithms, and the determination of legal responsibility. AI has the potential to transform bankruptcy procedures in Albania, but it requires a cautious approach that balances technological innovation with the protection of the legal rights of the parties involved. In this regard, harmonization of legislation with European Union standards and its proper implementation represent the most critical issues in this domain.

    • Review of the Development of Modern Green Energy Generators and Systems

      pg(s) 96-103

      Green energy (GE) is a type of renewable energy (RE) that is obtained from natural sources that are constantly renewed and do not pollute the environment or do so minimally. The main green energy sources (GES), which are considered 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. Currently, more than 10% of the world’s primary energy consumption comes from green energy (GE) technologies, namely: hydropower with approximately 5.1%, followed by wind power with 2.5%, solar power with 1.5% and other types of green energy with 0.9%. The development of cheap, dominant and fast-growing technologies and the development of cheap, intelligent and smart green energy generators and systems will ensure the increase of all types of green energy (GE) in the future. The paper provides a brief review of the historical development of different types of green energy systems, (GES) with a special focus on the historical development of wind turbines (WT). It also provides an review and analysis of several types of intelligent and smart green energy systems (GES), which include: Internet of Things (IoT), intelligent and smart sensors and other components of systems.