Despite the increasing demand for IT professionals, many potential students remain reluctant to enroll in computer science-oriented degree programmes. While there are a number of previous studies examining specific factors influencing students’ enrollment decisions, there is a lack of a comprehensive synthesis that consolidates these insights across different contexts and methodologies. This study conducts a systematic literature review (SLR) combined with a quantitative meta-analysis to identify and synthesize key factors deterring students from choosing IT-related fields. By analyzing existing research, the study categorizes these deterrents into influential, partly influential, and less influential factors. The most significant barriers include a preference for other majors, perceived unsuitable career prospects, and a lack of interest in IT-related subjects. Additional barriers include perceived difficulty, high workload, and the belief that IT work lacks social interaction. These insights provide valuable guidance for educational institutions, policymakers, and industry stakeholders to develop strategies that enhance interest in computer science programmes.

Addressing the grand societal challenges of our time requires a profound transformation of engineering education. This paper introduces Engineering Education 5.0, a future-oriented framework that integrates sustainability, digitalization, and human-centered values. In parallel, Community-Engaged Learning (CEL) is presented as a transdisciplinary pedagogical approach fostering experiential, participatory, and socially responsible learning. Together, these paradigms aim to close the gap between theoretical knowledge and real-world application. Two case studies—the SAFE Living Lab and MOSA—implemented at Montanuniversität Leoben, demonstrate how CEL can enhance the development of key competences among learners and contribute to sustainable innovation. The initiatives exemplify how Higher Education Institutions (HEIs) can serve as catalysts for societal transformation through collaboration with industry, policy, and the public. The paper concludes by advocating for structural reforms in engineering education to embed participatory, inclusive, and action-oriented learning strategies that prepare engineers for the complex demands of the digital and green transition.

The increasing adoption of supportive technologies, such as exoskeletons and collaborative robots, in warehousing operations is reshaping the role of human operators. While these technologies aim to reduce physical strain and enhance efficiency, their impact on the human factor remains unclear, with diverse and fragmented assessment approaches and a lack of standardized evaluation criteria. This paper presents a PRISMA-based systematic literature review of 57 studies on supportive technologies in warehousing. The review provides a comprehensive overview of the experimental approaches used to evaluate physical, cognitive, perceptual, and psychosocial impacts In addition, it offers a structured classification of the technologies adopted, the warehouse activities they support, and the human dimensions they affect. The findings highlight current gaps in the literature and offer practical guidance for selecting appropriate assessment approaches in experimental studies. This work contributes to the development of human-centric approaches in warehousing and supports informed decision-making in both research and practice.

Predictive Maintenance (PdM) is an important technique used to ensure the reliability and efficiency of industrial equipment. In recent years, Machine Learning (ML) techniques have become increasingly vital to PdM, offering powerful tools for analyzing complex data patterns and making accurate predictions about equipment health. Machine learning also uncovers relationships between input variables and system responses that are difficult to capture with traditional physical or mathematical models. This study compares the performance of two machine learning models - Subspace k-Nearest Neighbors (kNN) and symbolic classification -for predicting faults in rotating machines based on vibration data. The results demonstrate that Subspace k-Nearest Neighbors (kNN) handles different environments (e. g., orientation, position of the motor) with 98% accuracy. Symbolic classification, particularly in the two-stage hierarchical approach, provides interpretable models and easy deployment while maintaining a similarly high level of accuracy. The study outlines trade-ofs between model complexity, accuracy, and interpretability, and provides guidance for selecting appropriate PdM techniques. Future work includes expanding the dataset to improve generalization and incorporating high-frequency sensors for broader applicability. This research contributes to bridging the gap between theoretical ML models and practical industrial applications.

The transition to a circular economy (CE) has become a critical objective in addressing the environmental and resource challenges associated with linear production and consumption models. Digital Product Passports (DPPs) have emerged as a key instrument in supporting this transition by enabling transparency, traceability, and informed decision-making across product lifecycles. This study presents a structured review of 155 publications to examine the current state of research on DPPs, with a particular emphasis on their implementation within the built environment. Findings indicate that while DPPs are gaining policy and industry attention, particularly within the European Union, several technical, organizational, and regulatory challenges remain. These include the lack of standardized data models, concerns over data privacy and intellectual property, and limited empirical validation of proposed frameworks. Nevertheless, emerging solutions such as modular ontologies, data spaces, blockchain integration, and digital twins offer potential pathways forward. By synthesizing current knowledge and identifying critical issues, this review contributes to the broader understanding of how DPPs may support sustainable practices and circularity in the built environment and beyond.