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

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.

Abstract
In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles' autonomous driving and optimal route calculation.

Abstract
Problem: Portable ECG/sEMG acquisition systems for telemedicine often lack application flexibility (e.g., limited configurability, signal validation) and efficient wireless data handling. Methodology: A modular biosignal acquisition system with up to 8 channels, 24-bit resolution and configurable sampling (1-4 kHz) is proposed, featuring per-channel gain/source adjustments, internal MUX-based reference drive, and visual electrode integrity monitoring; Bluetooth (R) transmits data via a bit-wise packet structure (83.92% smaller than JSON, 7.28 times faster decoding with linear complexity based on input size). Results: maximum 6.7 mu V-rms input-referred noise; harmonic signal correlations >99.99%, worst-case THD of -53.03 dBc, and pulse wave correlation >99.68% in frequency-domain with maximum NMSE% of 6e-6%; and 22.3-hour operation (3.3 Ah battery @ 150 mA). Conclusion: The system enables high-fidelity, power-efficient acquisition with validated signal integrity and adaptable multi-channel acquisition, addressing gaps in portable biosensing.