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. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model’s ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

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. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model's ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

Objective The objective of this study was to estimate the prevalence of frailty among individuals over the age of 65 using a simple frailty questionnaire, to describe the impact of its components, and to analyze the relationship between frailty and various scales of health-related quality of life, functional impairment, and other health-related outcomes. Method Data were obtained through direct interviews conducted by a nurse trained in the use of Actuasalud software, who recorded responses to the administered questionnaires. Frailty was categorized as fragile, non-fragile, or pre-fragile. Results The study found that 18.6% of the individuals were categorized as fragile, with the majority being women. While most factors were found to be related to frailty, nutritional status, quality of life, and instrumental activities were multidimensionally associated with frailty. Conclusion Monitoring of these factors by nurses may contribute to preventing and/or delaying the onset of frailty states.