David Gutierrez

Supplemental zinc in fattening lambs improves their health, performance, and meat quality. However, the Zn effect on grazing animals combined with different levels of concentrate should be known unknown. The objective was to evaluate the Zn-organic effect in the diet of grazing lambs supplemented with two levels of concentrate on growth, dry matter (DM) intake, carcass traits, body composition, meat quality, and fatty acid profile in Longissimus dorsi muscle. Twenty-eight lambs were used in a factorial arrangement of two levels of concentrate (C) feed intake (C-0.75 and C-1.5% of live weight) and two Zn-Met levels (0 and 80 ppm Zn kg−1 DM) on the grazing of Lolium perenne L. Digestibility and dry matter intake, weight gain, and productive performance were measured. At the end of the fattening period (90 d), the lambs were slaughtered and the carcass traits, body composition, instrumental quality, and lipid profile of meat were evaluated. The effect of treatment (T), measurement period (P), and T × P interaction was observed (p < 0.05) for dry matter intake (DMI). For the final live weight (FLW) and daily weight gain (DWG), there was an effect (p < 0.05) of T and P, with Zn-80 ppm + C-1.5% treatment being greater. The live weight at slaughter (LWS) and leg length (LL) showed an effect (p < 0.05) of C × Zn. Zn-80 ppm + C-1.5% treatment was higher in the kidney fat, empty body weight, carcass fat, fat and retained energy but lower in protein content (p < 0.05). The physicochemical characteristics and fatty acid content of meat were not affected (p > 0.05). It can be concluded that the concentrated-organic zinc synergy in grazing lambs improved the performance, weight gain, and body composition, which resulted in heavier carcasses with greater amounts of fat, protein, and energy deposited. Furthermore, the physical and chemical meat traits were not affected, but the n-3 fatty acid content and n-3/n-6 ratio in Longissimus dorsi was affected by the Zn level supplemented.

The concept of well-being of rural families is part of a theory under construction in which new theoretical elements are constantly being incorporated. This research aims to determine the influence of farmers’ knowledge on the well‑being of cocoa growing families in the departments of Santander, Huila, Meta and Caquetá, Colombia. Four categories of farmers were identified with different levels of knowledge in the management of cocoa cultivation obtained through a cluster analysis. The well-being of cocoa farmers, understood as the balance in the capital endowment of rural households, was obtained through the application of a semi-structured interview with 49 variables of human, cultural, social, political, natural, built, and financial capitals. The results show that cocoa knowledge is heterogeneous in the study area, with a slight improvement towards harvesting, post-harvest and transformation links. There is a positive relationship between cocoa knowledge and the well-being of cocoa farming families. Thus, producers with greater integral knowledge, with emphasis on post-harvest and bean transformation links, showed greater well-being. The Random Forest analysis identified that human capital (political, social, human, and cultural) made the greatest contribution to well-being. The findings show that cocoa knowledge contributes to the well-being of rural households to the extent that it favors vertical relationships (linkages with local governments) and horizontal relationships of producers (participation of association managers, sharing knowledge with friends, neighbors and partners, and cocoa training).

The concept of well-being of rural families is part of a theory under construction in which new theoretical elements are constantly being incorporated. This research aims to determine the influence of farmers’ knowledge on the well‑being of cocoa growing families in the departments of Santander, Huila, Meta and Caquetá, Colombia. Four categories of farmers were identified with different levels of knowledge in the management of cocoa cultivation obtained through a cluster analysis. The well-being of cocoa farmers, understood as the balance in the capital endowment of rural households, was obtained through the application of a semi-structured interview with 49 variables of human, cultural, social, political, natural, built, and financial capitals. The results show that cocoa knowledge is heterogeneous in the study area, with a slight improvement towards harvesting, post-harvest and transformation links. There is a positive relationship between cocoa knowledge and the well-being of cocoa farming families. Thus, producers with greater integral knowledge, with emphasis on post-harvest and bean transformation links, showed greater well-being. The Random Forest analysis identified that human capital (political, social, human, and cultural) made the greatest contribution to well-being. The findings show that cocoa knowledge contributes to the well-being of rural households to the extent that it favors vertical relationships (linkages with local governments) and horizontal relationships of producers (participation of association managers, sharing knowledge with friends, neighbors and partners, and cocoa training).

The present study looks for influences from the East in the figure of Marcel Proust. Specifically, from the philosophy that is born from the sermons of Siddhartha Gotama known worldwide as buddhism. After the publication of several articles by some commentators of À la recherche du temps perdu in which it is said that Proust and Buddha have common features, we wanted to focus on the life and work of Proust and to confirm whether the French writer was indeed influenced by these exotic truths and to culminate his work, like Buddha, in an Enlightenment. We will also bring together here the work of these Proustian commentators, since they have not previously been grouped together in a single investigation, just as their proposals on this subject have never been evaluated.

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts.