This paper presents a systematic literature review of using Machine Learning (ML) techniques in higher education career recommendation. Despite the growing interest in leveraging AI for personalized academic guidance, no previous reviews have synthesized the diverse methodologies in this field. To fill this gap, we analyzed 38 studies selected from an initial pool of 1296, using a combination of search phrases and digital libraries. Our findings reveal that Random Forest, Support Vector Machines (SVM), and Neural Networks are the most frequently employed models to improve the accuracy and personalization of career recommendations in higher education. These systems typically use academic performance, personal interests, and demographic data as the primary data types. The review also highlights key validation metrics like precision, recall, and F1-score, which reflect the effectiveness of these models. However, limitations were identified, such as the lack of access to open datasets and the scarcity of studies with longitudinal data that evaluate the long-term impact of recommendations. This systematic literature review provides a solid foundation for improving career recommendation systems using advanced ML techniques. It highlights the potential to revolutionize career guidance while also addressing ethical considerations and the necessity of integrating with traditional counseling approaches.

This project presents the design and implementation of an IoT-based system for early detection of Liquefied Petroleum Gas (LPG) leaks in residential environments. Three functional prototypes were developed, integrating gas sensors, microcontrollers, actuators, and wireless modules. The system achieved 89.48% service availability and response times under 2 s from leak detection to data storage and visualization. Operating under a distributed architecture, it enables continuous monitoring, automatic shut-off, and real-time alerts. The novelty lies in the applied integration of electronics, embedded systems, and automation into an affordable, replicable solution, that enhances household safety, promotes preventive behavior, and supports the adoption of affordable, replicable technologies in vulnerable domestic settings.

Controlling the microclimate is vital in fruit fly breeding. This project develops an automated IoT system for monitoring and controlling temperature and relative humidity in chambers, optimizing processes through accessible and flexible technology. It uses a multi-layer system starting with the application layer with the ESP32 for data acquisition and actuator control. The second layer is the network layer, and the perception layer uses VisionFive2 with MQTT and HTTP protocols for communication, as well as NodeRed for flow orchestration and MySQL for data management and storage. In the validation, there are absolute errors of ±0.434 °C and ±0.5 RH, which are values within the acceptable ranges for these applications.

El estudio analizó la integración de la tecnología CNC en el contexto de la Industria 4.0, evaluando beneficios, desafíos y tendencias futuras. Se realizó una encuesta y un estudio de caso para identificar beneficios como la mejora en la eficiencia, flexibilidad, reducción de costos y mejora de la calidad y trazabilidad de los procesos. Se destacaron desafíos organizacionales, incluyendo la resistencia al cambio, la falta de visión estratégica y las limitaciones presupuestarias. Las tendencias futuras incluyeron la integración de la fabricación aditiva, la adopción de tecnologías de realidad aumentada y virtual, y el creciente papel de la inteligencia artificial en la optimización de procesos CNC. Se concluyó que la integración de CNC con la Industria 4.0 ofrece un potencial significativo para mejorar la competitividad y eficiencia de la manufactura, siempre que se aborden adecuadamente los desafíos técnicos y organizacionales. Se sugirieron futuras investigaciones para desarrollar estrategias y mejores prácticas en esta área.

Este estudio exploró las innovaciones tecnológicas en la industria manufacturera, evaluando su impacto en la eficiencia, sostenibilidad y competitividad de los procesos de producción. A través de una revisión de literatura, se identificaron tecnologías importantes como la inteligencia artificial, la robótica avanzada y el Internet de las Cosas, las cuales demostraron ser fundamentales para la transformación de la manufactura. Los resultados indicaron que, aunque estas tecnologías ofrecieron mejoras significativas en productividad y gestión de recursos, enfrentaron desafíos de implementación debido a los altos costos y la complejidad técnica, especialmente en pequeñas y medianas empresas. Además, se observó un impacto positivo en la sostenibilidad, gracias a prácticas como la gestión energética y la fabricación aditiva, que contribuyeron a la reducción de la huella de carbono. Las proyecciones sugirieron que tecnologías emergentes como el Blockchain y los gemelos digitales aumentarán su adopción en los próximos años, facilitando la trazabilidad y seguridad en la cadena de suministro. Al final, la adopción de estas innovaciones representa un paso de suma importancia hacia una manufactura más eficiente y sostenible, aunque requiere estrategias inclusivas para superar las barreras actuales.

The study analyzed the integration of CNC technology in the context of Industry 4.0, evaluating benefits, challenges and future trends. A survey and a case study were conducted to identify benefits such as improved efficiency, flexibility, cost reduction, and improved quality and traceability of processes. Organizational challenges were highlighted, including resistance to change, lack of strategic vision and budget constraints. Future trends included the integration of additive manufacturing, the adoption of augmented and virtual reality technologies, and the growing role of artificial intelligence in CNC process optimization. It was concluded that the integration of CNC with Industry 4.0 offers significant potential to improve manufacturing competitiveness and efficiency, provided that technical and organizational challenges are adequately addressed. Future research was suggested to develop strategies and best practices in this area.