This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. ML models are used to predict compressive strength, while genetic algorithms optimize the mixture cost under quality constraints. Using a dataset of over 19,000 samples from a local ready-mix concrete producer, various predictive ML models were trained and evaluated regarding cost-effective solutions. The results show that the optimized mixtures meet the desired compressive strength range and are cost-efficient, thus having $50\%$ of the solutions yielding a cost below $98\%$ of the test cases. CatBoost emerged as the best ML technique, thereby achieving a mean absolute error (MAE) below 5 MPa. This combined approach enhances quality, reduces costs, and improves production efficiency in concrete manufacturing.

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Nowadays, it has been necessary to investigate battery storage systems as a part of the massification of renewable energies, with a particular emphasis on batteries, which are the most crucial components in these systems. In this study, the two-pulse method is applied to LiFePO₄ battery cells to test the effectiveness of this method in this chemistry, based on previous validations in lead-acid cells. As a result, approximate values for the state of health (SOH) and state of charge (SOC) of the battery are obtained, with an estimated average standard error of the mean (SEM) less than 10%. An innovative aspect of the method is the inclusion of Lithium-based chemistry to verify the method and the comparison of the SOH obtained with the strain and temperature of a cell. These measurements can help to complement the information on the state of health of the battery cells. The method’s applicability to lithium-ion cells has been confirmed, although it requires suitable equipment for its correct application. Not all equipment can deliver uniform and controlled current pulses. Finally, it is necessary to consider some restrictions as a minimum current of at least 15% of the battery capacity value is required. The initial characterization may take some time, although parameter values can be found in the literature for certain technologies like lead-acid. The parameter values vary depending on the chemistry.

Fiscal control and monitoring of public management are issues of great importance and relevance, as they focus on preventing and mitigating corruption, thus benefiting governments in their management and performance, and thus citizens. Given the large amount of data involved in the management and functioning of a government, it is pertinent to apply data analysis methods to contribute to fiscal control. This paper aims to investigate data management for this purpose, starting with a bibliometric study to demonstrate the relevance and pertinence of the current study. It examines existing data collection techniques that may facilitate such control, data storage considering its nature and the possibilities that contribute to this end, and data processing through techniques that allow obtaining adequate information for further analysis and decision making. The necessary architecture and interoperability between systems is also discussed, as the existence of large amounts and types of data in governments makes it crucial to promote these practices for accurate and truthful analysis. Finally, data visualization is addressed since this topic is highly relevant as it allows for the identification of anomalies, where proper visualization promotes appropriate analysis and thus informed decision making. This work is useful for modern audit and fiscal control entities, since data capture, storage, processing, interoperability, and visualization are crucial elements that collectively enhance the ability to supervise public expenditure effectively, ensuring that public funds are managed with the highest standards of transparency, accountability, and efficiency.