Enhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimized Climate Control.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies.

Development and Evaluation of a Software Designed by a Nursing and Technology Team to Assess the Health Status of Adults over 65 Years of Age.

Objective: This work sought to develop the Actuasalud platform as a useful tool for nursing that permits assessing health, in term of frailty, in population over 65 years of age.

Methods: For the design and development of Actuasalud, two working groups were formed: one from nursing with different profiles, to identify the scientific content and a computer science group responsible for the software programming and development. Both teams adapted the scientific content to the technology so that the tool would allow for population screening with detection of health problems and frailty states.