In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices.

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers.

Many network protocols such as Controller Area Network (CAN) and Ethernet are used in the automotive industry to allow vehicle modules to communicate efficiently. These networks carry rich data from the different vehicle systems, such as the engine, transmission, brake, etc. This in-vehicle data can be used with machine learning algorithms to predict valuable information about the vehicle and roads. In this work, a low-cost machine learning system that uses in-vehicle data is proposed to solve three categorization problems; road surface conditions, road traffic conditions and driving style. Random forests, decision trees and support vector machine algorithms were evaluated to predict road conditions and driving style from labeled CAN data. These algorithms were used to classify road surface condition as smooth, even or full of holes. They were also used to classify road traffic conditions as low, normal or high, and the driving style was classified as normal or aggressive. Detection results were presented and analyzed. The random forests algorithm showed the highest detection accuracy results with an overall accuracy score between 92% and 95%.

This study presents a two-stage object detection system specifically tailored for low-light conditions. In the initial stage, supervised deep learning image enhancement techniques are utilized to improve image quality and enhance features. The second stage employs a computer vision algorithm for object detection. Three image enhancement algorithms—ZeroDCE++, Gladnet, and two-branch exposure-fusion network for low-light image enhancement (TBEFN)—were assessed in the first stage to enhance image quality. YOLOv7 was utilized in the object detection phase. The ExDark dataset, recognized for its extensive collection of low-light images, served as the basis for training and evaluation. No-reference image quality evaluators were applied to measure improvements in image quality, while object detection performance was assessed using metrics such as recall and mean average precision (mAP). The results indicated that the two-stage system incorporating TBEFN significantly improved detection performance, achieving a mAP of 0.574, compared to 0.49 for YOLOv7 without the enhancement stage. Furthermore, this study investigated the relationship between object detection performance and image quality evaluation metrics, revealing that the image quality evaluator NIQE exhibited a strong correlation with mAP for object detection. This correlation aids in identifying the features that influence computer vision performance, thereby facilitating its enhancement.

Driving style and road traffic play pivotal roles in the development of smart cities, influencing traffic flow, safety, and environmental sustainability. This study presents an innovative approach for detecting road traffic conditions and driving styles using On-Board Diagnostics (OBD) data and smartphone sensors. This approach offers an inexpensive implementation of prediction, as it utilizes existing vehicle data without requiring additional setups. Two Artificial Neural Network (ANN) models were employed: the first utilizes a forward neural network architecture, while the second leverages bootstrapping or bagging neural networks to enhance detection accuracy for low-labeled classes. Support Vector Machine (SVM) is implemented to serve as a baseline for comparison. Experimental results demonstrate that ANNs exhibit significant improvements in detection accuracy compared to SVM. Moreover, the neural network with bagging model showcases enhanced recall values and a substantial improvement in accurately detecting instances belonging to low-labeled classes in both driving style road traffic.

Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenarios