Industry 4.0
Vol. 11 (2026), Issue 1

Table of Contents

  • TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

    • AI-Enhanced Cybersecurity in Critical Infrastructures: A TRITON Framework and Review

      pg(s) 3-7

      Artificial intelligence (AI) is reshaping the field of penetration testing by enabling faster, more adaptive simulations of cyber threats. This paper explores how AI can be ethically integrated into penetration testing processes, focusing on the European Defence Fundbacked TRITON project. TRITON proposes a comprehensive AI-driven framework for testing the security of military and critical infrastructure systems, combining technologies like machine learning, generative models, and reinforcement learning. The TRITON project reviews recent advancements in AI-supported pentesting and discusses how these tools can improve vulnerability detection, attack simulations, and threat modeling. Alongside the technical discussion, which is examined in the TRITON project, ethical concerns—including transparency, human oversight, and dual-use risks—are of the essence and must be addressed to ensure responsible use. Comparisons with other EU cybersecurity initiatives, such as AI4CYBER and CyberSecDome, highlight TRITON’s unique contributions and focus areas. Ultimately, it argues that AI-enhanced penetration testing can significantly strengthen cybersecurity defenses when implemented with appropriate safeguards and ethical oversight.

    • An AI-Assisted Digital Guide for Cultural and Religious Tourism Integrating Interactive Maps and Text-to-Speech Technologies: A Case Study of Svishtov

      pg(s) 8-11

      This study presents the development of an AI-assisted digital guide designed to support cultural and religious tourism through the integration of interactive mapping and text-to-speech technologies. The project focuses on the cultural heritage of the city of Svishtov, Bulgaria, where several historically significant temples and monasteries are organized into a structured digital cultural route. Historical and descriptive information about each site was collected from local historiography and field observations and subsequently transformed into a digital format suitable for web presentation. An interactive map created in Google Maps allows users to visualize the spatial distribution of the sites and navigate the cultural route. In addition, an AI-generated audio guide based on text-to-speech technologies provides accessible audio narration for each location, enhancing the interpretive experience for visitors. The platform integrates visual content, geographic navigation, and automated voice narration within a single digital environment. The proposed approach demonstrates how artificial intelligence tools and interactive mapping technologies can support the interpretation of cultural heritage, improve the accessibility of historical information, and create engaging digital experiences for visitors. The study highlights the potential of combining digital humanities approaches with modern AI technologies for the promotion of local cultural heritage and the development of innovative tourism applications.

  • DOMINANT TECHNOLOGIES IN “INDUSTRY 4.0”

    • Machine Learning Prediction of Mechanical Properties for Al-Mg-Si Alloys Using a Hybrid Data Synthesis Approach

      pg(s) 12-14

      This study develops a machine learning framework for predicting the mechanical properties of 6xxx series Al-Mg-Si alloys (6061, 6063, 6082) across seven temper conditions. A hybrid dataset of 860 real measurement-based and 4,200 Monte Carlo augmented samples was generated from a literature-mined dataset. Random Forest (RF), Gradient Boosting (GBR), and Multilayer Perceptron (MLP) models were evaluated via 5-fold cross-validation (CV). RF achieved the best or comparable accuracy: coefficient of determination R² = 0.80 (ultimate tensile strength), 0.92 (yield strength), and 0.83 (elongation). Feature importance analysis showed that alloy type and temper encodings dominated predictions (>90% combined), while individual compositional features contributed <3% a result partly attributable to the augmentation strategy, which decoupled measured compositions from their corresponding property values. Learning curve analysis confirmed model convergence above 2,000 samples.

  • BUSINESS & “INDUSTRY 4.0”

    • A unified architectural framework for retail AI agents based on tiered functional decomposition

      pg(s) 15-19

      The rapid emergence of autonomous AI agents in commerce is reshaping how consumers interact with retailers, how decisions are made, and how value is exchanged across digital ecosystems. Yet despite accelerating innovation, the retail sector lacks a unified architectural model that defines how such agents should ingest information, reason about complex commercial contexts, and execute actions in a transparent, interoperable, and trustworthy manner. This paper introduces a unified architectural framework for retail AI agents based on tiered functional decomposition, offering a structured, three-tier model (Input, Model, Output) that standardizes agent behavior across diverse retail environments.
      The Input Tier formalizes how agents acquire and normalize information, including user intent, preference profiles, product catalogs, pricing signals, inventory data, logistics constraints, and retailer policies. By defining a consistent intake layer, the framework ensures that agents operate on coherent, comparable, and semantically aligned data structures.
      The Model Tier provides the cognitive core of the agent. It encompasses goal decomposition, relevance scoring, multi-objective optimization, negotiation logic, risk assessment, and alignment safeguards. This tier transforms raw inputs into structured decisions, enabling agents to act as rational, preference-aligned economic participants capable of navigating complex retail ecosystems.
      The Output Tier defines how agents act on decisions and how they learn from outcomes. It includes action execution (e.g., purchasing, reserving, scheduling), transparent justification, user-agent dialogue, cross-agent communication, feedback ingestion, and adaptive preference updating. This tier closes the loop between perception, reasoning, and action, enabling continuous improvement and long-term alignment with user goals.
      Together, these tiers form a modular, interoperable architecture that can be adopted by retailers, marketplaces, and technology providers to ensure predictable, explainable, and scalable agent behavior. The framework supports progressive capability levels, enabling retailers to evolve from informational agents to fully autonomous purchasing systems. By establishing a shared architectural foundation, this work aims to accelerate the development of agentic commerce and foster a more efficient, transparent, and user-aligned retail ecosystem.

    • Artificial intelligence in auditing and compliance in the telecommunications industry: A comparative empirical study of global operators

      pg(s) 20-23

      Telecommunications operators usually operate in highly competitive and highly regulated, data-intensive environments characterized by complex technological IT and NT infrastructure, high level of transaction volumes, and cross-border regulatory obligations. Traditional audit and compliance models — usually and largely based on sample-driven reviews in different time periods — are increasingly insufficient to provide timely and overall assurance. This article analyses the application of Artificial Intelligence (AI) tools in auditing and compliance within six global telecom operators and discusses adoption models, outcomes, and governance challenges.

  • SOCIETY & ”INDUSTRY 4.0”

    • Improving the Living Environment in Evacuation Shelters Using Light-Emitting Diode Lanterns and Partitions

      pg(s) 24-29

      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.

    • Using Large Language Models for Emotional Support of Bulgarian Users: A Survey

      pg(s) 30-33

      The use of large language models (LLMs) for psychological and emotional support (ES) has rapidly evolved, becoming the most widely used application of generative artificial intelligence among consumers by 2025. This paper presents the results of an anonymous survey of 100 Bulgarian users, primarily high school, university, and doctoral students, to explore their attitudes toward and usage of chatbots for emotional support. Findings indicate that approximately one-half of the surveyed population utilizes chatbots for ES, with ChatGPT being the most dominant platform. Users primarily seek support for coping with stress in interpersonal relationships and work or study-related environments. While 71% of users perceive the technology as effective, non-users remain sceptical. Despite the growing adoption, significant concerns persist regarding data security, technology reliability, and the tendency of chatbots to provide excessive affirmation.

    • Artificial intelligence supporting human decision-making in technological crises

      pg(s) 34-37

      Technological crises refer to critical events—system failures and collapses, cybersecurity and data breaches, software meltdowns, critical infrastructure disruptions, etc.,—in which the failure, malfunction, or disruption of technological systems creates significant risk to safety, operations, or the environment. These crises typically arise unexpectedly and require rapid, well-informed decision-making under conditions of uncertainty, time pressure, and missing or incomplete information, to prevent escalation. Artificial intelligence (AI) systems have emerged as powerful tools capable of augmenting human judgment in these contexts. This article examines the role of AI in supporting human decision-making during technological crises, analyzing its capabilities, limitations, and implications for organizational resilience. It emphasized that AI is most effective when used as a collaborative partner rather than a replacement for human expertise, and it outlines design principles for trustworthy, human-centered AI systems.

    • AI-based technologies in the authentication of fine art: toward a hybrid epistemology of cultural trust

      pg(s) 38-41

      The authentication of fine art has customarily relied on expert connoisseurship, material analysis, and provenance research. In recent years, artificial intelligence (AI) and AI-based technologies have appeared as significant tools in this domain, enabling new forms of algorithmic evidence, probabilistic reasoning, and large-scale pattern recognition. This paper examines how AI-based systems support museums, galleries, collectors, and private institutions in authenticating fine art paintings. It argues that AI does not replace human expertise but establishes a hybrid epistemic framework in which algorithmic forensics and art-historical knowledge co-produce authenticity. The study analyses key technological approaches, institutional applications, epistemological implications, and structural drawbacks, positioning AI as a catalytic agent in reconfiguring trust, authority, and knowledge production within the contemporary art ecosystem.

    • Artificial intelligence as a tool for systemic modelling and simulation of architectural strategies for a sustainable future

      pg(s) 42-45

      In this work, AI is examined as a tool for the simulation of climate scenarios and the systemic modelling of the future of the built environment under conditions of climate change. Rather than relying on numerical physical simulation, the approach is based on qualitative analysis using a set of possible future scenarios, in which the same building is considered within different climatic and economic contexts up to the end of the twenty-first century. The study examines how deteriorating climatic conditions redirect resources from development toward the compensation of losses and how this transforms the architectural environment over time. The paper discusses the potential for buildings to act as active participants in the carbon balance through mechanisms for CO₂ removal and long-term fixation. AI supports the analysis of causal relationships between climate scenarios, economic incentives, and architectural decisions in order to avoid systemically problematic strategies. The approach is applicable both in professional practice and as an educational exercise for architecture students.