Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.

Abstract
Quality inspection inspection systems are critical for maintaining product integrity. Being a repetitive task, when performed by operators only, it can be slow and error-prone. This paper introduces an automated inspection system for quality assessment in casting aluminum parts resorting to a robotic system. The method comprises two processes: filing detection and hole inspection. For filing detection, five deep learning modes were trained. These models include an object detector and four instance segmentation models: YOLOv8, YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, and Mask R-CNN, respectively. Among these, YOLOv8s-seg exhibited the best overall performance, achieving a recall rate of 98.10%, critical for minimizing false negatives and yielding the best overall results. Alongside, the system inspects holes, utilizing image processing techniques like template-matching and blob detection, achieving a 97.30% accuracy and a 2.67% Percentage of Wrong Classifications. The system improves inspection precision and efficiency while supporting sustainability and ergonomic standards, reducing material waste and reducing operator fatigue. © The Author(s) 2025.

Abstract
Artificial intelligence is gaining attraction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the public via the OpenAI web platform. It gains popularity in a very short period because of its real-world problem-solving capability. Considering the widespread use of ChatGPT and the people relying on it, this study determined how reliable ChatGPT can be used for learning in the medical domain. The capability of ChatGPT was evaluated using the questions of Harvard University gross anatomy and the United States Medical Licensing Examination (USMLE). The outcome of the ChatGPT was analyzed using a 2-way ANOVA and post-hoc analysis. Both tests showed systematic covariation between format and prompt. Furthermore, the physician adjudicators independently rated the outcome’s accuracy, concordance, and insight into the answers given by ChatGPT. As a result of the analysis, ChatGPT-generated answers were more context-oriented and represented a better model for deductive reasoning than regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical questions and 60% on ethical questions. This means that the ChatGPT is approaching the passing range for logical questions and has crossed the threshold for ethical questions. These results indicate that ChatGPT and other language-learning models can be invaluable tools for e-learners. © 2025 Elsevier B.V., All rights reserved.

Abstract
This study explores the application of the Lean Inception methodology in developing “EcoRider: Green Adventure,” an educational game aimed at enhancing motorcycle safety and promoting environmental awareness. Funded by the A-MoVeR project under the European Recovery and Resilience Facility, the game educates players on advanced safety technologies such as radars, cameras, LiDAR, and artificial intelligence (AI) algorithms. Players navigate complex urban scenarios, learning to manage potential hazards and promoting ecofriendly urban mobility. Using a qualitative case study approach, the research evaluates the effectiveness of integrating these technologies into the game’s design and gameplay. The game features multiple levels with increasing difficulty, requiring players to strategically place sensors and use AI models to overcome challenges. The application of the Lean Inception methodology has been essential in aligning the development team’s efforts, ensuring a cohesive approach to delivering a minimum viable product that satisfies both educational and technological objectives. Future work will be on refining the game, expanding its scope and exploring additional applications in the wider context of sustainable and safe mobility. © 2025 Elsevier B.V., All rights reserved.

Abstract
Facial expression is a way of communication that can be used to interact with computers or other electronic devices and the recognition of emotion from faces is an emerging practice with applications in many fields. Many cloud-based vision application programming interfaces are available that recognize emotion from facial images and video. In this article, the performances of two well-known APIs were compared using a public dataset of 980 images of facial emotions. For these experiments, a client program was developed that iterates over the image set, calls the cloud services, and caches the results of the emotion detection for each image. The performance was evaluated in each class of emotions using prediction accuracy. It has been found that the prediction accuracy for each emotion varies according to the cloud service being used. Similarly, each service provider presents a strong variation of performance according to the class being analyzed, as can be seen in more detail in these articles. © 2025 Elsevier B.V., All rights reserved.

Abstract
Canine hip dysplasia (CHD) screening relies on radiographic assessment, but traditional scoring methods often lack consistency due to inter-rater variability. This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. Unlike most AI models that directly classify CHD severity using convolutional neural networks, this system provides an interpretable, measurement-based output to support a more transparent evaluation. The system combines a keypoint regression model for femoral head center localization with a U-Net-based segmentation model for acetabular edge delineation. It was trained on 7967 images for hip joint detection, 571 for keypoints, and 624 for acetabulum segmentation, all from ventrodorsal hip-extended radiographs. On a test set of 70 images, the keypoint model achieved high precision (Euclidean Distance = 0.055 mm; Mean Absolute Error = 0.0034 mm; Mean Squared Error = 2.52 x 10-5 mm2), while the segmentation model showed strong performance (Dice Score = 0.96; Intersection over Union = 0.92). Comparison with expert annotations demonstrated strong agreement (Intraclass Correlation Coefficients = 0.97 and 0.93; Weighted Kappa = 0.86 and 0.79; Standard Error of Measurement = 0.92 to 1.34 mm). By automating anatomical landmark detection, the system enhances standardization, reproducibility, and interpretability in CHD radiographic assessment. Its strong alignment with expert evaluations supports its integration into CHD screening workflows for more objective and efficient diagnosis and CHD scoring.