Emotions as an axis in the quality of university education, and engine for economic development

Promover calidad de la educación universitaria es fundamental para el desarrollo, sin embargo sostenerlo solo desde currículos tecnificados, es olvidar el valor que agrega lo emocional en la formación de personas con responsabilidad social.El presente artículo reflexiona cómo la incorporación de lo emocional como un eje curricular, favorece la efectividad y profundidad de los aprendizajes, aunque su manejo en el aula obligue a un cambio en la relación profesor-alumno.Una experiencia de aula, basado en lo propuesto por Krathwohl confirmó mejoras en la disposición al trabajo, conductas y resultados en los grupos. Esto, en concordancia con la literatura, corrobora que una educación que prioriza competencias genéricas, consigue mayor desarrollo y productividad, formando profesionales que contribuirán mejor al desarrollo económico de su comunidad.Promoting the quality of university education is fundamental for development, but sustaining it only from technical curriculums, is forget the value that add the emotional aspects in the formation of people with social responsibility invisible.The present article reflects how the incorporation of emotional aspects as a curricular axis, favors the effectiveness and depth of the learning, although its handling in the classroom forces a change in the teacher-student relationship.A classroom experience, based on what Krathwohl proposed, confirmed improvements in the willingness to work, behavior and results in the groups. This, in agreement with the literature, corroborates that an education that prioritizes generic competences, achieves greater development and productivity, forming professionals that will contribute better to the economic development of their community

Prediction of the Limiting Flux and Its Correlation with the Reynolds Number during the Microfiltration of Skim Milk Using an Improved Model

Limiting flux (JL) determination is a critical issue for membrane processing. This work presents a modified exponential model for JL calculation, based on a previously published version. Our research focused on skim milk microfiltrations. The processing variables studied were the crossflow velocity (CFV), membrane hydraulic diameter (dh), temperature, and concentration factor, totaling 62 experimental runs. Results showed that, by adding a new parameter called minimum transmembrane pressure, the modified model not only improved the fit of the experimental data compared to the former version (R2 \u3e 97.00%), but also revealed the existence of a minimum transmembrane pressure required to obtain flux (J). This result is observed as a small shift to the right on J versus transmembrane pressure curves, and this shift increases with the flow velocity. This fact was reported in other investigations, but so far has gone uninvestigated. The JL predicted values were correlated with the Reynolds number (Re) for each dh tested. Results showed that for a same Re; JL increased as dh decreased; in a wide range of Re within the turbulent regime. Finally, from dimensionless correlations; a unique expression JL = f (Re, dh) was obtained; predicting satisfactorily JL (R2 = 84.11%) for the whole set of experiments

Modeling Tool for Studying the Influence of Operating Conditions on the Enzymatic Hydrolysis of Milk Proteins

Systematic modeling of the enzymatic hydrolysis of milk proteins is needed to assist the study and production of partially hydrolyzed milk. The enzymatic hydrolysis of milk proteins was characterized and evaluated as a function of the temperature and protease concentration using Alcalase, Neutrase and Protamex. Modeling was based on the combination of two empirical models formed by a logarithmic and a polynomial equation to correlate the kinetic constants and the operating conditions. The logarithmic equation fitted with high accuracy to the experimental hydrolysis curves with the three proteases (R2 > 0.99). The kinetic constants were correlated with the operating conditions (R2 > 0.97) using polynomial equations. The temperature and protease concentration significantly affected the initial rate of hydrolysis, i.e., the kinetic constant a, while the kinetic constant b was not significantly affected. The values for the kinetic constant a were predicted according to the operating conditions and they were strongly correlated with the experimental data (R2 = 0.95). The model allowed for a high-quality prediction of the hydrolysis curves of milk proteins. This modeling tool can be used in future research to test the correlation between the degree of hydrolysis and the functional properties of milk hydrolysates

Calculation of statistic estimates of kinetic parameters from substrate uncompetitive inhibition equation using the median method

We provide initial rate data from enzymatic reaction experiments and tis processing to estimate the kinetic parameters from the substrate uncompetitive inhibition equation using the median method published by Eisenthal and Cornish-Bowden (Cornish-Bowden and Eisenthal, 1974; Eisenthal and Cornish-Bowden, 1974). The method was denominated the direct linear plot and consists in the calculation of the median from a dataset of kinetic parameters Vmax and Km from the Michaelis–Menten equation. In this opportunity we present the procedure to applicate the direct linear plot to the substrate uncompetitive inhibition equation; a three-parameter equation. The median method is characterized for its robustness and its insensibility to outlier. The calculations are presented in an Excel datasheet and a computational algorithm was developed in the free software Python. The kinetic parameters of the substrate uncompetitive inhibition equation Vmax, Km and Ks were calculated using three experimental points from the dataset formed by 13 experimental points. All the 286 combinations were calculated. The dataset of kinetic parameters resulting from this combinatorial was used to calculate the median which corresponds to the statistic estimator of the real kinetic parameters. A comparative statistical analyses between the median method and the least squares was published in Valencia et al. [3]