This paper presents the design, implementation, and experimental validation of a granular jamming-based soft gripper for precision manipulation of delicate food products. Addressing the critical challenge of automating the handling of thermosensitive objects (e.g., chocolate), this work develops a vacuum-actuated gripper filled with ground coffee that conforms adaptively to irregular geometries without inducing damage. The system comprises a spherical granular membrane (maximum capacity: $\text{3 5 0 ~ g}$), a KUKA KR10 robotic arm, and closed-loop control via pneumatic actuation. Experimental validation consisted of $\text{1 0 0}$ pick-and-place trials at three distinct operational speeds ($\text{5 0 \%}, \text{7 5 \%}, \text{1 0 0 \%}$) using chocolate bonbons of five different geometries. Results revealed a speed-accuracy trade-off: at 50 % speed, the gripper achieved $99.56 \% \pm 0.36 \%$ success with 10.41 s cycle time; at 75 % speed, performance degraded to $97.53 \% \pm 1.14 \%$ with 9.10 s cycle time; at maximum speed (100 %), success dropped to $91.10 \% \pm 1.73 \%$ with 7.37 s cycle time. Positioning error remained minimal ($0.66-1.17 ~\text{mm}$ across all speeds). Critical analysis reveals that $\text{7 5 \%}$ operational speed provides optimal balance between throughput (386 chocolates/hour) and reliability greater than 97 %. Compared to conventional rigid grippers, this granular approach demonstrates superior adaptability to product geometry variation and reduced product damage. Integration of real-time visual feedback could enable near- 100 % performance at higher speeds through corrective trajectory adjustment.

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.

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.