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- Connecting the dots – A permacultura aplicada à gestão de um projeto culturalPublication . Magalhães, Carla Joana Almeida de; Castro, Regina Maria de Carvalho Menezes e; Oliveira, Fernando Matos deInserida no campo de estudos que relaciona arte e sustentabilidade, a investigação desenvolvida tem como objeto de pesquisa as práticas artísticas contemporâneas, circunscritas às artes performativas, compreendendo quer os processos de “genética artística” quer os “modos de produção” a elas associados. Acompanhando a crescente atenção subordinada aos modos de produção nas artes e à sua “ecologia operacional”, procura entender como é que a arte se faz ecológica do ponto de vista processual, assegurando a sua “sustentabilidade” nas suas diferentes dimensões (ambiental, cultural, social e económica). Perseguindo esta questão, propõe-se investigar o conceito de jardim aplicado à gestão de um projeto artístico. Através da adoção de uma estratégia híbrida e em permanente oscilação entre practice-led research (pesquisa derivada da prática) e practice-based research (pesquisa baseada na prática) e research-led practice (prática derivada da pesquisa) (Smith & Dean, 2009), aplica-se a metáfora vegetal ao projeto artístico “O jardim” - projeto transdisciplinar ongoing - que se constitui estudo aplicado. Fazendo uso de uma metodologia de investigação-ação, pesquisa-se um modo de gestão sustentável, baseado nos princípios da permacultura. Desta forma, procuram-se modelos de gestão alternativos, decorrentes de um discurso da prática, com o objetivo de contribuir para o debate sobre como fazer crescer futuros abundantes e sustentáveis perante a catástrofe ecológica.
- Transfer learning applied to government auditing: A focused approach on financial statements in Maranhão, BrazilPublication . Coelho, Heloisa Guimarães; Marreiros, Maria Goreti CarvalhoSince Brazil’s return to democracy, dozens of laws, decrees and normative instructions have been drafted with the purpose of regulating and improving the mechanisms for controlling and monitoring municipal public resources. These regulations are specifically aimed at the process of accountability by elected officials, who currently rely on the help of accountants responsible for preparing and submitting financial statements to the Courts of Auditors. However, according to data from the TCU (Federal Court of Accounts), in 2023, Maranhão was the Brazilian State with the highest number of rejected accounts. There are several reasons that can lead to these processes being challenged, including incorrect application of resources, flaws in documentation, human errors, among others. In practice, the routine of accountants includes repetitive and mechanical activities that requires considerable time to prepare and review documents, hence often leading to errors in classification and issuing of documentation. In this context, this dissertation investigates the use of Transfer Learning (TL) to improve automation and accuracy in the classification of financial commitment notes, an initial document in the public expenditure cycle, with a specific focus on the context of the state of Maranhão. To this end, BERTimbau, a pre-trained language model for Brazilian Portuguese, was fine-tuned to assist government accountants in reducing classification errors and ensuring compliance with local and national financial regulations. The CRISP-DM methodology, widely used in data science, was adopted to structure the development of the project. The dataset used, consisting of several classifications of commitment notes for the year 2023, was thoroughly analyzed and pre-processed. For the fine-tuning process of the model, two samples with a similar number of data were selected, varying only the number of possible classifications, due to the high degree of imbalance between the classes. Even in a multiclass context with datasets with a reduced number of classes, the results obtained indicate that the BERTimbau model presents strong performance in the classification task, achieving 98% accuracy with an error rate of 0.10 in the test set, highlighting the effectiveness of BERTimbau in public financial auditing applications. These results highlight the effectiveness of BERTimbau for public financial auditing applications. It is therefore concluded that TL models have great potential to optimize and improve financial auditing processes, with positive implications for wider adoption in Brazil.
- Performance analysis of steel W18CR4V grinding using RSM, DNN-GA, KNN, LM, DT, SVM models, and optimization via desirability function and MOGWOPublication . Fernandes, Filipe; Touati, Sofiane; Boumediri, Haithem; Karmi, Yacine; Chitour, Mourad; Boumediri, Khaled; Zemmouri, Amina; Moussa, AthmaniThis study presents an innovative approach to optimizing the grinding process of W18CR4V steel, a high-performance material used in reamer manufacturing, using advanced machine learning models and multi-objective optimization techniques. The novel combination of Deep Neural Networks with Genetic Algorithm (DNN-GA), K-Nearest Neighbors (KNN), Levenberg-Marquardt (LM), Decision Trees (DT), and Support Vector Machines (SVM) was employed to predict key process outcomes, such as surface roughness (Ra), maximum roughness height (Rz), and production time. The results reveal significant improvements, with Ra values ranging from 0.231 μm to 1.250 μm (up to 81.5 % reduction) and Rz from 1.519 μm to 6.833 μm (up to 77.7 % reduction). The hybrid DNN-GA model achieved R2 > 0.99, reducing prediction errors by 23–45 % compared to traditional models. Optimization via the Desirability Function achieved Ra values around 0.341 μm and Rz around 2.3 μm, with production times ranging from 1181 to 1426 s. The innovative Multi-Objective Grey Wolf Optimization (MOGWO) provided Pareto-optimal solutions, minimizing Ra to 0.3 μm, Rz to 1.5 μm, and production times between 2000 and 3000 s, offering better balance between surface quality and machining efficiency. This work highlights the unique integration of machine learning models with optimization techniques to significantly enhance grinding performance and manufacturing efficiency in high-precision industries.
- Improvement of Surface Properties and Wear Resistance of Selective Laser Melting-Fabricated Inconel 625 Alloy by Ultrasonic Nanocrystal Surface Modification for Demanding ApplicationsPublication . Fernandes, Filipe; Fathipour, Zahra; Hadi, Morteza; Bayat, Omidnconel 625 alloy is widely utilized in the production of components for demanding industries. This study investigates the effect of ultrasonic nanocrystal surface modification (UNSM) on the surface properties and wear resistance of Inconel 625 alloy produced by selective laser melting (SLM). Specifically, it focused on analyzing the effect of UNSM on the microstructure, hardness, surface roughness, coefficient of friction and essentially wear resistance of the alloy. The results showed that the microstructure formed by SLM, characterized by relatively large melt pools, was modified by UNSM to a depth of approximately 10 to 15 microns, resulting in a new microstructure composed of deformed grains without changing the chemical composition. Surface hardness increased by over 63% after UNSM treatment. In addition, the surface roughness initially induced by the SLM process was reduced by more than 90%, resulting in a tenfold reduction in the coefficient of friction. Wear path analysis showed that while the abrasive wear mechanism of the alloy remained unchanged, the UNSM treated samples exhibited increased debris production and more frequent delamination due to reduced workability. The alterations in surface properties, including reduced crystallite size, increased lattice strain, grain refinement, and decreased surface area, have been identified as key contributors to the enhanced hardness and wear resistance of the alloy following UNSM treatment.