Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

This paper presents a detailed analysis of the relation between physical characteristics and defects of green coffee beans and the sensory profile that influence the sensory notes of fragrance, aroma, flavor, and aftertaste of coffee. Machine learning models were used to identify the variables of importance and identify the ways in which these variables affect the sensory note of coffee, to determine which algorithm and its hyperparameters have greater precision in determining the sensory values of coffee such as floral, fruity, herbal, nutty, caramel, chocolate, spicy, resinous, pyrolytic, earthy, fermented, and phenolic. The result indicates the relationship and importance that exist between the physical variables, defects, and size of the green coffee bean, with respect to their respective sensory notes. The data of the proposed system demonstrate that by combining the scores of several experts, a precision can be achieved analogously to that obtained by cupping experts; therefore, the possibility of errors induced by human concerns such as fatigue or subjectivity is reduced.

A sugar solution measurement system was developed based on the dielectric properties of the sucrose molecule. An ac conductivity and tan δ study as a function of the frequency was performed to find the suitable frequency range for the measuring system. The results indicate that it is possible to obtain a better response of the sensor using the frequencies as the maxima peak in tan δ appears. Developed setup for sucrose solution was appropriate to measure in a 0.15 to 1 g/mL range with an experimental error of about 3%. The proposed system improves the measurement time over some other methods.

This paper presents a novel biomechatronic device that resolves the necessities of mobility for people with spinal cord trauma (SCI) and disability. The proposed device features a safe and reliable mobility mechanism that withstands daily use without premature mechanical wear, facilitating the activities of daily living (ADL) for people affected by SCI, integrating them to a social and workforce environment that allows them, on one hand, to move in a standing upright position in complex situations of the urban architecture, and on the other hand, provides them a mechatronic system to assist them to stand up and sit down.

Sugarcane is one of the main agro-industrial products consumed worldwide, and, therefore, the use of suitable soils is a key factor to maximize its production. As a result, the need to evaluate soil matrices, including many physical, chemical, and biological parameters, to determine the soil’s aptitude for growing food crops increases. Machine learning techniques were used to perform an in-depth analysis of the physicochemical indicators of vertisol-type soils used in sugarcane production. The importance of the relationship between each of the indicators was studied. Furthermore, and the main objective of the present work, was the determination of the minimum number of the most important physicochemical indicators necessary to evaluate the agricultural suitability of the soils, with a view to reducing the number of analyses in terms of physicochemical indicators required for the evaluation. The results obtained relating to the estimation of agricultural capability using different numbers of parameters showed accuracy results of up to 91% when implementing three parameters: Potassium (K), Calcium (Ca) and Cation Exchange Capacity (CEC). The reported results, relating to the estimation of the physicochemical parameters, indicated that it was possible to estimate eleven physicochemical parameters with an average accuracy of 73% using only the data of K, Ca and CEC as input parameters in the Machine Learning models. Knowledge of these three parameters enables determination of the values of soil potential in regard to Hydrogen (pH), organic matter (OM), Phosphorus (P), Magnesium (Mg), Sulfur (S), Boron (B), Copper (Cu), Manganese (Mn), and Zinc (Zn), the Calcium/Magnesium ratio (Ca/Mg), and also the texture of the soil.

Vegetable oils are considered to be eco-friendly and to offer good lubricant properties; however, their low thermo-oxidative stability makes their use as a lubricant base challenging. In this research, sunflower, almond, and rapeseed vegetable oils were added in volumes of 5, 10, 15, and 20% to a machinery oil, and the thermal properties of the resulting fluids were studied. Sunflower, almond, and rapeseed oils were chosen considering their fatty acid composition and the tocopherol content. During this investigation, thermal diffusivity was measured by using the thermal wave resonance cavity technique, while thermal effusivity was determined by the inverse photopyroelectric method, and the obtained values ranged from 4.63 to 5.75 Ws^1/2m⁻²K⁻¹ × 10². The thermal conductivity was calculated by obtaining a complete thermal characterization. The results showed a linear relationship between the percentage of vegetable oil and the thermal diffusivity. It was also noted that the thermal properties of diffusivity and effusivity could be tuned when using almond, sunflower, and rapeseed oils in the appropriate percentages. Hence, the influence of vegetable oils on the thermal properties of lubricating oil were closely related to the number of fatty acids.