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

Contemporary architecture is crucial for post-socialist urban identity formation of cities in transition, and in particular of Tirana. During the last thirty years, the city has undergone a transition from the centralized socialist model into diverse, complex and ambivalent metropolitan space. This research examines the relationships between contemporary architecture and urban identity formation in Tirana using a mixed-methods approach, incorporating qualitative spatial analysis and quantitative data based on the citizens’ perceptions analysis. The qualitative analysis explores three main urban interventions (Skanderbeg Square, the Pyramid of Tirana, the “Deshmoret e Kombit” Boulevard), and the quantitative analysis is based on the survey findings. The results suggest that Tirana‘s urban identity has become increasingly hybrid, through interaction of historical layers and global modern influences, and continues to develop new identities. But it is in tension with the forces that seek to preserve existing sources of urban identity and to modernise the city. This research increases the discussion on post-socialist cities and proposes an analytical framework of three perspectives, based on citizens’ perceptions [7], [10], [14].

This study presents a framework for the digital profiling and online representation of traditional Bulgarian belt buckles (pafti) through the integration of structured cultural data and accessible 3D visualization approaches. The research is positioned within the context of Industry 4.0, where digital transformation, data-driven methodologies, and virtual representation increasingly influence the preservation and interpretation of cultural heritage. Traditional ethnographic artifacts are often documented through fragmented textual descriptions and static photographs, limiting their accessibility and analytical potential in digital environments. The proposed approach combines ethnographic documentation with data structuring and digital visualization techniques. Cultural attributes such as regional affiliation, ornamentation, material composition, decorative elements, and object form are organized into a structured descriptive model suitable for digital analysis and presentation. In parallel, the study applies a low-cost photogrammetry workflow for creating a digital representation of selected artifacts, supported by online visualization through a web-based environment and QR-based access. The results demonstrate how traditional cultural objects can be transformed into structured digital entities that support visualization, accessibility, and interactive exploration. The study highlights the potential of combining cultural heritage research with digital technologies in order to create accessible models for the preservation and presentation of ethnographic artifacts within contemporary digital ecosystems.

The digital transformation of healthcare systems is significantly changing the way healthcare organizations operate and healthcare is delivered. The rapid development of digital technologies has increased the demand for digital competencies among healthcare professionals and managers. These competencies are essential for the effective implementation and use of digital technologies in healthcare. The aim of this study is to identify and analyze the key digital competencies required in healthcare organizations in the context of digital transformation. A systematic literature review was conducted using the PRISMA methodology to identify relevant scientific publications focused on digital competencies in healthcare. The analysis focuses on the digital skills and competencies of healthcare professionals and managers. The findings highlight the importance of digital literacy, data management, information security, and continuous professional development. The study contributes to an understanding of how healthcare organizations can better prepare their employees for digital transformation.

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.

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.

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.

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.

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.

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.

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.

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.

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.