Association of the chemical composition and nutritional value of forage resources in Colombia with methane emissions by enteric fermentation.

In the livestock sector, strategies are available to mitigate gas emissions, such as methane, one of the alternatives that have shown potential correspondence to changes in the composition of the diet. The main aim of this study was to analyze the influence of methane emissions with data on enteric fermentation obtained from the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database and based on forecasts of methane emissions by enteric fermentation with an autoregressive integrated moving average (ARIMA) model and the application of statistical tests to identify the association between methane emissions from enteric fermentation and the variables of the chemical composition and nutritional value of forage resources in Colombia. The results reported positive correlations between methane emissions and the variables ash content, ethereal extract, neutral detergent fiber (NDF), and acid detergent fiber (ADF) and negative correlations between methane emissions and the variables percentage of unstructured carbohydrates, total digestible nutrients (TDN), digestibility of dry matter, metabolizable energy (MERuminants), net maintenance energy (NEm), net energy gain (NEg), and net lactation energy (NEI). The variables with the most significant influence on the reduction of methane emissions by enteric fermentation are the percentage of unstructured carbohydrates and the percentage of starch. In conclusion, the analysis of variance and the correlations between the chemical composition and the nutritive value of forage resources in Colombia help to understand the influence of diet variables on methane emissions of a particular family and with it in the application of strategies of mitigation.

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.