Abstract
Information Systems (IS) projects are critical for organizational development, both in the private and public sectors. The relevance and complexity inherent in this type of project require management to be fully aware of the factors that influence success. This study contributes to the literature on public-sector IS project management by providing a comprehensive set of Success Factors (SFs) for different levels of the public administration. The research method comprised a literature review, six case studies of central government, local government, and other types of administration, and a questionnaire-based survey of public sector IS experts. Forty-four SFs were identified, described, and organized in nine categories: organization and environment; strategy; project; scope; project manager and project team; stakeholders; vendors; clients and users; and monitoring and control. Our results add a new perspective to the theoretical body of knowledge on the SFs for IS projects in the public sector. © 2025 Copyright held by the owner/author(s).

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