Abstract
Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored. Copyright (C) 2024 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
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper aims to provide an overview of the impact of the COVID-19 pandemic on control engineering education worldwide. The authors, who are educators in the control education field from various countries across all continents, first summarize their experiences to present a global perspective on the different solutions adopted in control education during the pandemic. Afterwards, collected information from the international community through a questionnaire enabled insightful comparisons between pre-pandemic and during-pandemic educational resources and methods, which are shared in this paper. The feedback from the authors’ experiences, along with the questionnaire responses, serves as a valuable resource for learning and improving teaching activities. The questionnaire was distributed among the international control engineering community in collaboration with the International Federation of Automatic Control (IFAC) to explore the diverse alternatives employed globally for conducting online educational activities during the pandemic. These activities include methodologies, tools, theoretical exercises, laboratory experiments, exam types, simulators, and software for online lecturing.

Abstract
Encouraging pre-university students to pursue engineering courses at the university level is essential to meet the industry’s escalating demand for engineers. Each year, universities host hundreds of secondary students who tour their facilities to get a feel for the academic environment. This paper discusses an educational experiment designed as part of a semester-long undergraduate project in Informatics Engineering. The project involves tailoring a Dobot Magician robot, equipped with a standard webcam, to engage in a game of tic-tac-toe against a human user. The camera stream is continuously processed by a computer vision algorithm to detect the pieces placement in the game board. The paper outlines the project development stages, the elements involved, and presents preliminary test results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
To meet the growing demand of robotics applications in industry high efforts are placed on research and education for robotics fundamentals, as a more intuitive and easier access to robotics must be ensured. Based on this, the aim of this work is to make a contribution to investigate new approaches for teaching robotics fundamentals using immersive technologies. In this work an existing digital twin for a desktop robot was extended by adding a feature for the positioning of way-points for path planning in an existing Augmented Reality (AR) application as well as update the robot controller with the inverse kinematics for the corresponding motion implementation. The performance of the new functionality was evaluated by means of a pick-and-place application and validated via a test series with an associated questionnaire. The conducted experiments have been carried out in a comparative study between the AR based digital twin and an ABB industrial robot where a clear benefit of the proposed systems for entry into robotics as well as for teaching was identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations' efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.