The convergence of Information Technology (IT) and Operational Technology (OT) introduces complex cybersecurity challenges, particularly for industrial control systems. This paper presents a security framework that integrates industry security standards into the Purdue model, offering a structured approach to safeguarding IT-OT networks. By mapping security controls from various security standards like ISO 27001, IEC 62443, NIST SP 800-82, and ISO 27033 to the individual Purdue model levels, this framework establishes a security baseline which focuses on small and medium-sized enterprises (SMEs) to enhance their network resilience. The proposed approach emphasizes layered security mechanisms, including network segmentation, access control, encryption, and incident response. In addition, risk assessment methodologies are applied to prioritize security measures, optimizing protection strategies against emerging threats. The implementation guidelines are tailored to address practical constraints in SMEs, ensuring accessibility and effectiveness. The findings underscore the importance of adopting a structured security framework to mitigate cybersecurity threats in industrial environments, aligning IT and OT security postures.
Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection methods within FL paradigm poses data-centric challenges. The existing datasets do not counteract these challenges since they do not simultaneously provide sufficient scale, accurate labels, and freedom from common flaws. In addition, the role of cyclic process behavior, which is common in discrete industrial automation, remains underexplored for MTSAD for the current state of research. This paper aims to shed more light on the literature and address these gaps by introducing a dataset designed with cyclic dynamics arising from the repetitive nature of discrete automation processes and evaluates selected MTSAD methods on both the proposed dataset and a public benchmark dataset.
International collaboration in allied health and nursing education faces a long-standing paradox: although healthcare practice is increasingly global, professional education remains largely national or local. Regulatory constraints, the historical framing of health professions as task-based rather than academic, gendered perceptions of nursing as supportive rather than leadership-oriented, and structural inequities all limit cross-border collaboration and physical student mobility. In response, the CRIISIS (Connecting and Reflecting in Student International Interactive Study Groups) Collaborative Online International Learning (COIL) model was developed to reimagine internationalization through equity, relational learning, and reciprocity. Grounded in Kolb’s Experiential Learning Theory, the model integrates students’ personal and family health crisis narratives into a structured cycle of reflection, comparative analysis, and action within international teams. Students engage in virtual proximity, intentional relationship-centered interaction across distance, using dialogue, shared inquiry, and collaborative problem-solving to connect individual experiences to global health challenges and the United Nations Sustainable Development Goals. Evaluation of three implementation cycles demonstrates that the CRIISIS COIL model fosters cultural humility, communication adaptability, leadership within complexity, and narrative competence. Students develop deeper awareness of how social and structural factors shape health and family experiences across societies. By valuing lived experience as legitimate knowledge and promoting horizontal partnership across institutions, the model challenges traditional hierarchies in global education. Ultimately, CRIISIS positions global learning not as an extractive exchange, but as an ethical, relational, and equity-oriented process that prepares health professionals to navigate and address health inequities in an interconnected world.
BackgroundClimate change increasingly alters environmental conditions. Heat phenomena negatively impact human health, because heat stress increases heat strain in vulnerable populations such as cardiovascular rehabilitation patients and potentially affects rehabilitation procedures and outcomes.ObjectiveThe RehabHeat study investigates the spatiotemporal patterns of minimum and maximum ambient outdoor temperatures at rehabilitation facilities across Austria over the past three decades. The focus is on seasonal variations and extreme heat events to enable an informed discussion of potential implications for rehabilitation practices.MethodsTemperature data from 64 selected rehabilitation facilities in Austria were analyzed, particularly focusing on Kyselý days (≥3-day periods with maximum temperatures >30.0 °C), Tropical nights, and seasonal temperature trends. Spatial clustering was used to identify regional differences in thermal trends, and temporal patterns were assessed on a 10-year basis, also considering seasonal fluctuations.ResultsThe analysis revealed a considerable increase in temperature extremes, particularly in Austria's eastern regions, with significant rises in both Kyselý days and Tropical nights. 2019, 2023, and 2024 rank among the hottest years within the past three decades, underscoring an intensifying climatic trend. Seasonal analysis revealed that winter temperatures (average increase in daily maximum air temperature from Decade 1 to 3: 1.93 °C) have risen to a magnitude similar to summer warming (1.92 °C). Autumns were characterized by a protracted transitional phase (1.64 °C), whereas spring exhibited the weakest warming (0.79 °C).ConclusionsAustria's changing thermal landscape, marked by increasing heat extremes and seasonal shifts, challenges to rehabilitation sites. Adaptation measures seem urgently warranted to mitigate health risks, particularly in vulnerable patients and high-burden regions.
Purpose This study aims to examine how organizational factors arising from isomorphic pressures – and individual perception factors of perceived ease of use and perceived usefulness – influence the adoption of artificial intelligence (AI) in management accounting. By exploring cross-country cases from the United States, Germany and Austria, it seeks to uncover the mechanisms through which forces shape firms’ decisions, providing empirical insights into organizational responses and contextual variations in AI implementation. Design/methodology/approach Findings are based on an exploratory, qualitative research design using semistructured interviews. Data were analyzed through within- and cross-case analysis, applying deductive coding. Findings Adoption is driven by distinct isomorphic pressures across the USA, Germany and Austria. Mimetic pressures emerge from competitive necessity and leadership vision, while coercive pressures are exerted through regulatory compliance and client return on investment demands. Normative pressures focus on professional standards and data security. Internal strategic goals moderate responses, highlighting cross-national differences. While institutional pressures initiate adoption, the long-term integration of AI is contingent upon high levels of perceived usefulness and ease of use. At the same time, adoption is shaped by organizational frictions, validation burdens and the risk of ceremonial compliance, dynamics that are constitutive of the adoption process rather than merely incidental to it. Practical implications Understanding mimetic, coercive and normative forces helps organizations anticipate external expectations, align strategies and address barriers such as data security, skill shortages and resistance to change, fostering effective AI integration. Particular attention should be paid to governance and oversight mechanisms as preconditions for substantive adoption, and to the risk that formal compliance with institutional pressures may produce ceremonial rather than genuine integration. Originality/value To the best of the authors’ knowledge, this study is among the first to combine institutional theory and technology acceptance model with empirical evidence, it provides novel cross-national insights and expands understanding of organizational responses to technological transformation. It further challenges predominantly efficiency-oriented accounts by demonstrating that organizational frictions and ceremonial adoption are analytically co-equal dimensions of AI-related change.
