Abstract
Since the public unveiling of ChatGPT-3 in November 2022, its impact and consequences for society have been significant. This generative artificial intelligence has now become a disruptive technology. Education in general, and Engineering Education in particular, are feeling the effects of the widespread adoption of artificial intelligence tools by students. However, teachers and universities are still struggling with how to deal with these technologies. The current increase in digitalisation makes detecting unauthorised use of ChatGPT and similar tools a major challenge. This paper therefore explores several issues regarding the use of ChatGPT in the context of Engineering Education. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
Portable, pocket-sized laboratories offer a cost-effective means for students to conduct control experiments outside the classroom. Broad access to such laboratories can help bridge the gap between theoretical knowledge and practical application. The Temperature Control Laboratory (TCLab) is one such portable kit that has been effectively utilized for teaching and learning control engineering. Building on experience with TCLab since 2018, we propose a unified experiment focused on PID control. This experiment was integrated into a Modeling and Control Engineering course for Biomedical Engineering undergraduates at UTAD. The students' feedback indicates strong interest and underscores the value of this handson experience. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
Rapidly evolving scientific and technological advances are introducing both exciting and disruptive educational tools. However, they also present new challenges in engaging and motivating students, particularly in courses with a strong mathematical foundation like control engineering. The first class of any course offers a prime opportunity to make a lasting impression that encourages active learning. This paper addresses the following question: How can the first feedback control class be transformed into a unique and memorable event that leaves a positive impact on students for the remainder of the course, and perhaps, ambitiously, for their lives? Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Abstract
This paper addresses the persistent rise in motorcycle-related fatalities, even as overall road deaths decline, by introducing an adaptive Fuzzy System based on the Takagi-Sugeno model. The system evaluates parameters such as acceleration and lean angle to classify rider behavior into categories such as normal, aggressive, or dangerous, providing timely feedback aimed at promoting safer driving practices. A key component of this approach is the Local Outlier Factor (LOF) algorithm, which identifies hazardous behaviors by quantifying deviations from standard riding patterns, thereby allowing the establishment of adaptive safety thresholds. By integrating fuzzy logic, the system offers refined decision-making capabilities in complex riding conditions, enhancing active safety systems such as traction and braking controls. This work emphasizes the critical role of behavior-based insights in mitigating accidents, particularly since rider actions are a major contributing factor to motorcycle incidents.

Abstract
Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings.