By 2030, the world’s population is projected to reach 8.5 billion, posing significant challenges for food production. Traditional agriculture, which requires large amounts of water, soil, and energy, can contribute to the depletion of natural resources and environmental degradation. In this context, organic hydroponic systems emerge as a sustainable alternative, allowing for more efficient, controlled, and resilient production in the face of climate change. In this research, the physical development of romaine lettuce and the physicochemical parameters of the crop water are evaluated as a function of the number of daily recirculations. The crop variables are measured with the help of an intelligent control system, which allows the real-time monitoring of the process variables. The methodological approach is mixed: quantitative, for the recording of physicochemical variables, and qualitative, for the physical analysis of the crop throughout the process, With the experiments conducted it was found that the treatment with four daily recirculations promoted the most significant physiological growth of the plants. Despite having a pH of approximately five and dissolved oxygen of 6 mg/L, this treatment maintained adequate levels of TDS (2050 ppm) and hardness (1000 ppm), favoring the development of the crop. The treatments with less recirculation presented lower growth values. These results suggest that increased recirculation can optimize yields in floating-root hydroponic systems, addressing global food challenges from an environmentally responsible perspective.

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture.

Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R², and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m², MSE = 849.96, R² = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change.