This article tackles the critical challenge of optimizing the buffer pool, a core component of Database Management Systems (DBMS) that caches frequently accessed data pages, where manual configuration often proves inadequate in dynamic, high-demand environments. To address this gap, we present an automated, data-driven methodology that combines advanced Machine Learning techniques with Bayesian optimization. Our approach follows a systematic three-phase process: (1) Exploratory Factor Analysis (EFA) coupled with K-means clustering to uncover latent factors and reduce the dimensionality of performance metrics; (2) LASSO regression to identify and rank the most influential configuration parameters; and (3) Bayesian optimization using Gaussian Process modeling with acquisition functions (Expected Improvement, Probability of Improvement, and Upper Confidence Bound) to fine-tune buffer pool settings. The main contributions of this work include a novel automated framework for DBMS tuning that simplifies configuration, enhances memory management, and boosts performance efficiency. We validated the proposed solution using real workloads collected from a large-scale financial system in Latin America, achieving up to a 45% reduction in maximum data access wait times, confirming improvements in performance and scalability.

This paper explores Protocol Buffers as a serialization mechanism for Distributed Interactive Simulation (DIS) Protocol Data Units (PDUs) to enhance performance in distributed simulation communications. The DIS protocol is a widely used standard for connecting various simulators across geographically dispersed locations, utilizing PDUs for communication. The experiment compares the performance of Entity State PDUs (ESPDU) serialization using the standard DIS method against Protocol Buffers, focusing on encoding time, decoding time, packet transmission rate, and packet loss. A client/server application prototype was developed, and tests were conducted in a LAN environment with messages being generated at one message per millisecond. The results indicated the Protocol Buffers had good performance with 56.96 % less decoding time. However, DIS still showed better performance in encoding time, being 41.20 % faster. Future research could explore combining C-DIS strategies with Protocol Buffers to leverage compact serialization and improve military simulation networking performance.

This paper results from a project for promoting students’ interest in the field of computing through teaching computer science, robotics, and programming in Brazilian public schools. Here, we present a mixed-methods study conducted with elementary and secondary school teachers involved with such a project. The study analyzes teachers’ perceptions regarding the project’s impact on their professional trajectories, on the students, and on the school and community contexts in which they operate. The findings highlight the importance of early exposure to computing as a strategy to bridge the gap between educational levels and as a tool for inclusion and social transformation. The results indicate that participation in the project has generated positive outcomes. Teachers report the development of new skills—particularly technical skills in computing—increased motivation for teaching, and the opportunity to make a meaningful impact on students’ lives. Furthermore, they observe positive changes among the girls, especially in terms of learning and engagement, as well as improvements in the school and community environments, including greater parental involvement in the students’ educational journey.