Sergio Martin

Distance learning imposes specific challenges to provide quality education. Technology is a great opportunity for educational improvement. In this work we analyze some challenges and we have shown that with the proper use of technology it is possible to overcome them. We introduce here a set of remote laboratories currently being used for control engineering education in our university, which contribute to teach several control engineering topics like perception systems and the use of sensors and actuators, cybersecurity and penetration testing of IoT smart sensors and similar devices that can be found in industry, computer system architecture and low-level programming of microprocessors to implement fast control algorithms. The value of this work lies not only in the utility of remote labs as learning tools but also in their implementation following IEEE standards and integrating modern devices and communication protocols with industrial application.

Construction activities within the Oil and Gas Industry present many challenges and inherent dangers for workers. Many current solutions available in the market lack real-time insights and predictive capabilities for proactive decision-making and operational safety. This industry demands robust solutions to enhance safety, improve security, and increase productivity, and this is where Human Digital Twins (HDTs), Wearables, and Artificial Intelligence (AI) play an important role. HDTs aim to create a dynamic digital replica of workers, integrating data from wearables, IIoT sensors, information systems, and any other data source available, enabling continuous monitoring of physiological and cognitive conditions. Complementing HDTs, AI-powered wearables collect enriched data from embedded sensors, such as heart rate, blood oxygen, or fatigue, and integrate deep learning algorithms to predict potential incidents directly on the edge before they happen, thanks to their high computing capabilities. This review represents the state-of-the-art for HDTs and AI-powered wearables for the Oil and Gas industry. It explores current technological developments, their applications, the challenges and limitations of deploying them into a real-world project, and also synthesizes current research and industry practices, highlighting their potential in human health and safety. Additionally, it identifies future research directions to overcome existing barriers.

Massive Open Online Courses (MOOCs) have established a new paradigm in education, enabling asynchronous, remote learning. Although MOOCs offer diverse educational content to students, comprehensive and realistic education requires hands‐on training. Therefore, we present our integration of remote laboratories within a MOOC focused on Industry 4.0, together with a mixed‐methods analysis combining demographic data, questionnaire responses, platform logs, and remote laboratory server records. The course incorporated seven distinct laboratories in two categories: remote Arduino‐based laboratories, which connected physical devices to the MOOC, and a virtual infrastructure based on JupyterHub to support practical activities. Our findings indicate that the majority of participants in our MOOC were actively employed, with a mean age of 46.9 years, and males representing 88%. The students were mainly motivated by personal and professional growth, and 66% of these learners had no prior remote laboratory experience. Learners reported generally positive perceptions of the laboratory experience, including high levels of interest, usefulness, and self‐efficacy, although engagement varied substantially across participants. Behavioral analyses revealed that students who ultimately pursued certification showed markedly higher participation and involvement in the remote laboratories than those who did not. At the same time, the course exhibited the substantial dropout patterns commonly reported in MOOCs, indicating that the inclusion of remote laboratories does not by itself eliminate persistence challenges. Our work provides an illustrative example of integrating remote laboratories into a MOOC and offers an analysis of enrolled students, delivering valuable findings for future researchers.

El desarrollo de la inteligencia artificial (IA) generativa es uno de los campos emergentes en la tecnología educativa en los últimos años. Por ello, este trabajo persigue diseñar dos herramientas de IA generativa, basadas en chatbots educativos, que contribuyan a la mejora de los procesos de enseñanza-aprendizaje en educación superior, así como evaluar su impacto desde la perspectiva de los usuarios de un MOOC ofrecido por la UNED, considerando el efecto de variables sociodemográficas y de experiencia previa de uso de IA. Para ello, tras diseñar las dos herramientas, se ha realizado una investigación de carácter ex post facto, no experimental, en la que se han recogido y analizado las percepciones de 1.302 personas usuarias del MOOC. Los resultados reflejan una valoración positiva de las potencialidades en estas herramientas en relación con la satisfacción general y la contribución al aprendizaje, además de una tendencia a rechazar la sustitución de humanos por chatbots educativos. Por su parte, el análisis del papel de las variables sociodemográficas y de uso de la IA muestra una especial importancia tanto de la familiaridad con la IA como de la edad en estas percepciones. En conclusión, ambos chatbots parecen ser herramientas con un gran potencial para contribuir al desarrollo de los procesos de enseñanza-aprendizaje en educación superior, lo que se evidencia a través de los elevados niveles de satisfacción de los participantes encuestados. Sin embargo, resulta fundamental continuar desarrollando y actualizando estas herramientas, tomando en consideración las valoraciones de los usuarios, para maximizar su potencial educativo.

The state of health (SOH) of a Li-ion battery is determined by complex interactions among its internal components and external factors. Approaches leveraging deep learning architectures have been proposed to predict the SOH using convolutional networks, recurrent networks, and transformers. Recently, Mamba selective state space models have emerged as a new sequence model that combines fast parallel training with data efficiency and fast sampling. In this paper, we propose SambaMixer, a Mamba-based model for predicting the SOH of Li-ion batteries using multivariate time signals measured during the battery’s discharge cycle. Our model is designed to handle analog signals with irregular sampling rates and recuperation effects of Li-ion batteries. We introduce a novel anchor-based resampling method as an augmentation technique. Additionally, we improve performance and learn recuperation effects by conditioning the prediction on the sample time and cycle time difference using positional encodings. We evaluate our model on the NASA battery discharge dataset, reporting MAE, RMSE, and MAPE. Our model outperforms previous methods based on CNNs and recurrent networks, reducingMAEby 52%, RMSE by 43%, and MAPE by 7%.

The state of health (SOH) of a Li-ion battery is a critical parameter that determines the remaining capacity and the remaining lifetime of the battery. In this paper, we propose SambaMixer a novel structured state space model (SSM) for predicting the state of health of Li-ion batteries. The proposed SSM is based on the MambaMixer architecture, which is designed to handle multi-variate time signals. We evaluate our model on the NASA battery discharge dataset and show that our model outperforms the state-of-the-art on this dataset. We further introduce a novel anchor-based resampling method which ensures time signals are of the expected length while also serving as augmentation technique. Finally, we condition prediction on the sample time and the cycle time difference using positional encodings to improve the performance of our model and to learn recuperation effects. Our results proof that our model is able to predict the SOH of Li-ion batteries with high accuracy and robustness.