A multidisciplinary approach is crucial for developing a user-adaptive gamified learning system, starting with pedagogy and continuing through psychology, user experience (UX) design, and artificial intelligence (AI). This study inspects different layers of knowledge, starting from foundational pedagogical theories such as constructivism, behaviourism, and Bloom's taxonomy. Moving to individual learner needs with all related complexities, which include cognitive traits, learning styles, personality types, motivational drivers, and player types, is important. Thus, the gamified learning experience can be tailored for optimal engagement and effectiveness. The gamified learning experience is influenced by a set of factors, including learners evolving traits and experiences. A user system must track and interpret these changes in real time to maintain interaction and effectiveness. Modern Technological tools like AI and machine learning (ML) can be used to design personalized predictive models. Human-computer interaction (HCI) through UX and interaction design is crucial for maintaining learners’ engagement throughout the learning process. The complexity of user-adaptive processes requires the use of HCI via UX design. This study proposes a framework that integrates andragogy, psychology, and intelligent technologies, such as AI and ML, to create a personalized learning system. This approach optimizes the learning process, allowing learners to develop their knowledge and skills and enhance productivity, efficiency, and work quality. It focuses on professional retraining for longer-tenured workers who have been away from formal education for several years and require customized learning paths to close the digital skill gap essential for operating in smart, sustainable manufacturing environments.

In the global transition towards renewable energy, a leading role is played by photovoltaic (PV) technologies. However, the increasing growth of installed PV panels, together with the rise of the number of modules reaching their end-of-life phase, make the sustainable management of electronic waste a crucial aspect. The reduction of energy consumption and polluting emissions and the maximization of material recovery represent the ultimate purpose of demanufacturing processes. Here, cryogenic delamination is proposed as an innovative strategy, as it exploits the thermal and mechanical properties of PV module constituents to achieve the cleanest possible separation of layers, allowing for the recovery of strategic materials (silicon, aluminium, silver, copper). This work aims to combine experimental and numerical approaches in order to obtain a comprehensive understanding of the fundamental mechanisms governing the process: the overall objective is represented by the process optimization to enable the exploration of various operating conditions without the need for costly and time-intensive experimental campaigns and, ultimately, the implementation of such technology at the industrial scale.

In the era of Industry 4.0, the focus on optimizing manufacturing processes is crucial. This study provides a comprehensive analysis of the EnPAS system, a proprietary automated control and monitoring tool developed by KAD3, implemented in the production process of Corten steel furnishings - the "Lila" chair and "Line" planter. Designed to enhance operational efficiency, EnPAS facilitates improved workflow by streamlining production stages and reducing downtime. The research design involves a comparative analysis of the workshop's production process, both before and after the implementation of the EnPAS system. Elements such as de-greasing, oxidation, and finishing stages, among others, were examined. The implementation of the EnPAS system led to a significant reduction in the total production time for both products. Further, there was a substantial decrease in waiting time and setup time. These findings unequivocally indicate the transformative impact of the EnPAS system on the manufacturing process, showcasing its potential in driving efficiency improvements, reducing production times, and increasing output. The study underscores the importance of leveraging such advanced control and monitoring tools to optimize modern manufacturing processes and does not exaggerate conclusions not backed by the research.