Nestor Abel Sánchez-Goycochea

Temperature abuse during storage represents a critical factor influencing microbial behavior in meat products, particularly in non-conventional matrices such as guinea pig meat. This study aimed to characterize and compare the primary growth kinetics of Aerobic mesophilic bacteria and Staphylococcus aureus (S. aureus) in guinea pig meat burgers under controlled temperature abuse conditions (30, 35, and 40 °C). Microbial growth was monitored over 96 h and described using the modified Gompertz model to estimate key kinetic parameters, including maximum specific growth rate (µ_max) and lag phase duration (λ). Aerobic mesophilic bacteria exhibited increasing µ_max values with temperature, indicating enhanced metabolic activity under elevated thermal conditions. In contrast, S. aureus showed reduced µ_max and prolonged λ at 40 °C, suggesting stress-induced modulation of growth dynamics. These findings demonstrate that temperature increases do not uniformly accelerate microbial proliferation across different populations within the same food matrix. The contrasting kinetic responses indicate that Aerobic mesophilic bacteria and S. aureus respond differently to temperature abuse conditions, highlighting that total aerobic counts alone may not reliably predict pathogen behavior in guinea pig meat burgers.

Fraudulent transactions represent a significant global issue due to their economic and social impact. This research aims to identify the most effective machine learning models for detecting fraudulent transactions. A systematic literature review was conducted as the primary methodology, structured around three specific questions derived from the main research question: Which machine learning models are the most effective for detecting fraudulent transactions? A total of 78 articles were analyzed, extracted from the Scopus and Web of Science databases up to September 2024. Of these, 39 met the inclusion criteria established under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. The results highlight that machine learning and deep learning models, such as Random Forest, Extreme Gradient Boosting, Long Short-Term Memory, Artificial Neural Networks, and some hybrid ML-DL models, are the most effective for detecting fraudulent transactions. It is concluded that these techniques provide robust and reliable solutions to prevent losses caused by fraud, contributing to the development of advanced strategies in organizations and financial sectors.

In their article "Coupling at a distance HDG and BEM", Cockburn, Sayas and Solano proposed an iterative coupling of the hybridizable discontinuous Galerkin method (HDG) and the boundary element method (BEM) to solve an exterior Dirichlet problem. The novelty of the numerical scheme consisted of using a computational domain for the HDG discretization whose boundary did not coincide with the coupling interface. In their article, the authors provided extensive numerical evidence for convergence, but the proof of convergence and the error analysis remained elusive at that time. In this article we fill the gap by proving the convergence of a relaxation of the algorithm and providing a priori error estimates for the numerical solution.

In their article “Coupling at a distance HDG and BEM”, Cockburn, Sayas and Solano proposed an iterative coupling of the hybridizable discontinuous Galerkin method (HDG) and the boundary element method (BEM) to solve an exterior Dirichlet problem. The novelty of the numerical scheme consisted of using a computational domain for the HDG discretization whose boundary did not coincide with the coupling interface. In their article, the authors provided extensive numerical evidence for convergence, but the proof of convergence and the error analysis remained elusive at that time. In this article we fill the gap by proving the convergence of a relaxation of the algorithm and providing a priori error estimates for the numerical solution.