Currently, setting the obligation to use respiratory protective equipment with a level of protection of at least FFP2 in public transport and commercial and shopping centers are considered within the European Union. Many new products appeared on the European market within these specifications, and many symbols have been used. The paper deals with the meaning of selected respiratory protection based on respirators symbols and markings (which appeared massively, especially after the pandemic situation in the spring of 2020), these symbols not being uniformly understood and well communicated. We also mention and discuss some of the problems related to setting the conditions of public procurement, which affect respirators. Thus, this review is an “informative guide” in facilitating the understanding and use of full knowledge of the most appropriate respiratory protective devices in various situations.

This scientific article examines the transformative potential of Agentic AI in Science, Technology, Engineering, and Mathematics (STEM) education. It highlights how Agentic AI can enhance learning outcomes in these subjects, reduce cognitive load, and better prepare students for the demands of an AI-driven workforce. By utilising smart tools like GitHub Copilot, Agentic AI systems provide opportunities to improve STEM learning environments through personalised assistance, collaborative problem-solving, and skill development, all while automating repetitive tasks. This paper also discusses the future implications of the job market influenced by Agentic AI, emphasising the need for upskilling and the necessity for educational systems to adapt to emerging roles. It also touches on ethical aspects like fair access and AI literacy while highlighting how Agentic AI could help bridge the gap between traditional education and real-world job readiness.

Universities increasingly align their activities with the Sustainable Development Goals, but institutional action, measurement, documentary evidence, external assessment, and substantive sustainability impact are not equivalent. This structured evidence-based narrative review with integrative conceptual synthesis examined university sustainability implementation, measurement, reporting, governance, assessment, and outcomes, using the 2026 Times Higher Education Sustainability Impact Ratings as the principal external-assessment exemplar. The review sample comprised heterogeneous peer-reviewed empirical, review, bibliometric, modelling, and case-based studies, while Times Higher Education methodological documentation and University of Oradea (UO) institutional sources were analysed separately. Across the synthesis, recurrent problems in translating institutional action into measurable and documentable performance were grouped into three diagnostic categories: performance gaps, measurement gaps, and evidence gaps, which formed the diagnostic core of the review-derived Institutional Action–Data–Evidence Framework. The framework links gap diagnosis to construct-valid measurement, evidence readiness, management review, and corrective action. It was subsequently illustrated through a documentary institutional case at UO. The case did not contribute to framework derivation and was not used as an empirical validation dataset. Principal limitations include heterogeneous source designs, reliance on publicly accessible and partly institution-generated case documentation, and the absence of prospective external multi-institutional validation. Practically, the framework provides a diagnostic for distinguishing an underlying performance problem from deficiencies in measurement or documentary evidence and for linking each diagnosis to a differentiated institutional response. Future research should evaluate the framework prospectively across multiple universities and longitudinally examine whether external-assessment performance corresponds to independently measured sustainability outcomes.

Artificial Intelligence (AI) is reshaping the Information Technology (IT) service management (ITSM) and endpoint technical support by automating routine and repetitive technical tasks, enhancing overall efficiency, and introducing new ways of delivering support. This paper takes a closer look at AI’s role in endpoint support management in an academic context, based entirely on peer-reviewed research. Our methodology in this article is based on a comparison between traditional and AI-based IT support models. The findings will show that AI tools like smart ticket data classifiers and chatbots can help solve issues faster, cut costs, and ease the workload on support staff in endpoint support, which has a direct impact on service level agreements (SLAs). AI-powered ITSM solution systems can detect and resolve incidents, which means that they enable a proactive incident management approach and 24/7 support availability, rendering the traditional helpdesk model increasingly obsolete. However, the “downfall” or, better said, the “transformation” of current endpoint support is not without challenges. We discuss and describe the challenges of data privacy, bias, transparency, and the risk of workforce displacement, highlighting the need for ethical systems and IT staff and AI synergy. In current times, AI could redefine endpoint technical support management by ensuring responsible implementation and a human-centric approach, which will be vital for a sustainable success for support efficiency. Possible future directions could include hybrid support models that integrate AI automation with human expertise, well-defined ethical principles, and further research into AI’s long-term effects on service quality and digital workforce dynamics.