Arthur M. P. Gabardo

Mooring systems ensure the structural integrity of Floating Production Units, especially in deep waters. These structures face environmental conditions that cause displacements along the waterline, resulting in significant stresses on risers and other subsystems. Minimizing such displacements and loads is crucial for safety. The design of mooring systems is complex, requiring a thorough analysis of project characteristics and environmental factors to fulfill safety requirements. Even though advances in computational tools have enhanced offshore system design by enabling a holistic assessment of critical parameters, optimization remains computationally intensive and often constrained by available resources. In this context, this work proposes the adoption of High-Performance Computing in offshore system design, allowing distributed optimization processes and accelerating the evaluation of solutions. Our procedure was successfully validated in a real-world case study involving the repositioning of an operational Floating Production Unit through a distributed implementation of the NSGA-II optimization algorithm, resulting in the reconfiguration of its mooring system. A viable repositioning was achieved with 32.94 m of displacement in the desired direction (10.98 % of water depth) and a reduction of 7 % in maximum line tension in the mooring system. Altogether, execution time was reduced by tenfold relative to the serial implementation of NSGA-II.

Brazil faces significant traffic safety challenges with its vast territory and one of the world’s largest road networks. Road traffic accidents, particularly on federal highways, remain a leading cause of death in the country, with serious economic and social consequences. This work presents a case study of three machine learning methods — Random Forest (RF), k-Nearest Neighbors (kNN), and Multilayer Perceptron (MLP) — for classifying the severity of traffic accidents in the Brazilian southern region. Using an open dataset from the Brazilian Federal Highway Police (PRF) covering the years 2021 to 2024, extensive preprocessing was carried out, including categorical variable encoding, feature selection, and the application of the SMOTE technique to address class imbalance. Model performance was assessed through statistical metrics such as specificity, F1-score, and AUC-ROC. The results show that RF and kNN (with SMOTE) achieved the best performance in predicting fatal accidents, both with AUC-ROC of 0.99. In addition to an in-depth model evaluation, this study presents a post-hoc analysis of feature importance and contributions through Shapley Additive Explanations (SHAP) for the best performing model, in order to support knowledge discovery and highlight the most influential factors associated with fatalities.

A Otimização Multidisciplinar possui um papel central na integração de áreas diversas em projetos de engenharia. Recursos computacionais podem, porém, ser um fator limitante em processos de otimização. Este artigo apresenta um middleware desenvolvido para integrar o software Synapse a clusters gerenciados pelo Slurm Workload Manager. O middleware facilita a execução de algoritmos de otimização populacionais (e.g., algoritmos genéticos) de maneira distribuída, favorecendo processos de otimização. Testes em um cluster heterogêneo permitiram validar a solução. Em experimentos preliminares, a solução desenvolvida apresentou speedups de até dez vezes em relação ao uso de workstations, abordagem até então suportada pelo Synapse.