This paper presents an exploratory study on short-term international collaboration in engineering education conducted at the Portuguese Luban Workshop. The experiment involved engineering students from the Polytechnic Institute of Setúbal (Portugal) and the Federal Institute of São Paulo (Brazil), organized into distributed teams engaged in remote, peer-to-peer learning activities. Over a seven-day period, Portuguese students delivered technical instruction on industrial automation systems, while Brazilian students participated as learners. The study employed a pre–post design using Likert-scale questionnaires to evaluate technical understanding, communication, collaboration, and perceived learning outcomes. Statistical analysis was performed using paired t-tests and Wilcoxon signed-rank tests, complemented by effect size measures. Although the small sample size limited statistical significance, the results indicate meaningful educational benefits, particularly in communication skills, intercultural interaction, and teaching-based learning reinforcement. Qualitative feedback highlighted both strengths—such as effective knowledge exchange—and challenges, including limited time, connectivity issues, and the need for improved instructional structuring. The findings suggest that even short-term digitally mediated international collaboration can support human-centered skill development in engineering education, while also emphasizing the importance of careful design and scalability for future implementations.

Human motion prediction is a key component for enabling anticipatory behavior in human--robot shared environments, where systems must operate under dynamic and partially observable conditions. However, most existing approaches are evaluated under static sensing assumptions and aggregate metrics, which can obscure the factors that truly determine prediction performance in real deployments.
This work investigates human motion prediction under dynamic sensing conditions, focusing on how perception, scene composition, and human behavior jointly affect predictive reliability. A lightweight and training-free approach based on a customized Kalman Filter is proposed, integrating motion dynamics with orientation cues to improve short-term trajectory estimation while maintaining real-time performance and interpretability.
The system is implemented within a modular perception pipeline combining vision-based detection, tracking, and state estimation, and is validated in two laboratory setups with fixed and mobile robot-mounted sensing. Beyond overall performance, a structured evaluation stratifies results by worker motion state, number of agents, and prediction horizon.
Results show that prediction performance is strongly conditioned by sensing dynamics and human motion characteristics, with worker state emerging as the dominant factor and dynamic sensing introducing a consistent degradation across models. These findings expose a critical limitation of current evaluation practices, showing that aggregate metrics and static sensing assumptions can significantly overestimate real-world performance.

Saudi Aramco’s Operational Excellence Management System (OEMS) is a comprehensive framework designed to drive and sustain high performance across all operational domains. By integrating engineering governance with digital technologies, the platform enables real-time data acquisition from backend systems such as SAP, facilitating accurate and timely decision-making. This digital integration enhances visibility into asset performance, maintenance schedules, and compliance metrics, allowing for proactive management of risks and opportunities. The system supports standardized engineering procedures and ensures adherence to internal policies and global standards through automated workflows and digital checklists. Real-time KPI dashboards provide actionable insights, enabling leaders to monitor performance trends, identify gaps, and initiate corrective actions swiftly. Furthermore, the platform fosters a unified operational language across departments, promoting collaboration, consistency, and continuous learning. By embracing mobility solutions, cloud-based analytics, and integrated data architectures, Saudi Aramco strengthens its ability to achieve operational agility, improve asset reliability, and sustain excellence in a rapidly evolving industrial landscape.