Due to its extensive use in various livestock production activities and its use in human health care, antibiotics have become an environmental problem that draws the attention of the scientific community. Several research works report their presence in different ecosystems compartments, as well as their impact in organisms that inhabit these ecosystems which are being investigated by this community; until now, it has been reported problems of bacterial resistance and damages at DNA level in living beings, among others; in this perspective it is necessary to perform monitoring in various environmental matrixes, in order to detect and quantify their presence, to have a better understanding of their long-term effects on living beings. In this sense, it addresses aspects that determine their presence in the ecosystem, as well as shows results of works evaluating their removal of contaminated...

Due to its extensive use in various livestock production activities and its use in human health care, antibiotics have become an environmental problem that draws the attention of the scientific community. Several research works report their presence in different ecosystems compartments, as well as their impact in organisms that inhabit these ecosystems which are being investigated by this community; until now, it has been reported problems of bacterial resistance and damages at DNA level in living beings, among others; in this perspective it is necessary to perform monitoring in various environmental matrixes, in order to detect and quantify their presence, to have a better understanding of their long-term effects on living beings. In this sense, it addresses aspects that determine their presence in the ecosystem, as well as shows results of works evaluating their removal of contaminated...

Due to its extensive use in various livestock production activities and its use in human health care, antibiotics have become an environmental problem that draws the attention of the scientific community. Several research works report their presence in different ecosystems compartments, as well as their impact in organisms that inhabit these ecosystems which are being investigated by this community; until now, it has been reported problems of bacterial resistance and damages at DNA level in living beings, among others; in this perspective it is necessary to perform monitoring in various environmental matrixes, in order to detect and quantify their presence, to have a better understanding of their long-term effects on living beings. In this sense, it addresses aspects that determine their presence in the ecosystem, as well as shows results of works evaluating their removal of contaminated...

Due to its extensive use in various livestock production activities and its use in human health care, antibiotics have become an environmental problem that draws the attention of the scientific community. Several research works report their presence in different ecosystems compartments, as well as their impact in organisms that inhabit these ecosystems which are being investigated by this community; until now, it has been reported problems of bacterial resistance and damages at DNA level in living beings, among others; in this perspective it is necessary to perform monitoring in various environmental matrixes, in order to detect and quantify their presence, to have a better understanding of their long-term effects on living beings. In this sense, it addresses aspects that determine their presence in the ecosystem, as well as shows results of works evaluating their removal of contaminated...

The objective of this study is to predict the quantity of ANFO required for bench blasting in an open pit mine in Peru, through the application of advanced machine learning techniques. Six models were selected: Artificial Neural Networks (ANNMLP), Random Forests (RF), Support Vector Machines for Regression (SVR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (BR), due to their ability to handle complex multidimensional data and their success in similar applications, such as rock fragmentation prediction. The methodology included the collection of data from 208 drill holes, which were divided into training (70%), validation (15%), and testing (15%) sets. The models were evaluated using RMSE, MSE, MAE, and R2. The KNN model showed the best performance, with an R2 of 0.84, RMSE of 2.37, MSE of 5.60, and MAE of 1.35, standing out in predictive accura...