ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Ciências da Computação e da Informação"
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- Development of a Simulation Tool for Electromagnetism EducationPublication . Auwarter, Bianca; Brandão, Diego; Ferreira, Ângela P.The widespread adoption in the educational system of information and communications technologies allows the use of interactive simulations, able to support a meaningful insight into the fundamental laws and concepts of electromagnetic theory, which, in a classical approach, would require a vector calculus background and three-dimensional geometrical resourcefulness, typically not maturated by the students in undergraduate engineering programmes. The use of simulation tools based on finite element analysis can facilitate the learning process by allowing users to create and/or exploit visual and more accurate models. ONELAB (Open Numerical Engineering LABoratory) is a simulation platform that integrates severalmodelling tools, including Gmsh, a three-dimensionalmesh modelling software. This simulation tool is able to provide interaction, accuracy, and visual interpretations of classical problems using the fundamental laws of electromagnetism. The application with the basic laws of electromagnetism has been developed to run on mobile devices, besides desktops, to improve its ease of access and dissemination.
- Reconstruction of meteorological records with PCA-based analog ensemble methodsPublication . Breve, Murilo M.; Balsa, Carlos; Rufino, JoséThe Analog Ensemble (AnEn) method has been used to reconstruct missing data in time series with base on other correlated time series with full data. As the AnEn method benefits from the use of large volumes of data, there is a great interest in improving its efficiency. In this paper, the Principal Component Analysis (PCA) technique is combined with the classical AnEn method and a K-means cluster-based variant, within the context of reconstructing missing meteorological data at a particular station using information from neighboring stations. This combination allows to reduce the dimension of the number of predictor time series, while ensuring better accuracy and higher computational performance than the AnEn methods: it reduces prediction errors by up to 30% and achieves a computational speedup of up to 2x.
- Resonant recognition model as a preprocessing technique for RNA classificationPublication . Souza, Felipe Bueno de; Pimenta-Zanon, Matheus; Henriques, Dora; Pinto, M. Alice; Balsa, Carlos; Rufino, José; Fabrício Martins LopesThe development of high throughput sequencing technologies, such as RNA-Seq, has enabled the generation of large volumes of biological data. Thus, it is necessary to develop computational methods to interpret this massive volume of data and contribute to knowledge discovery. RNA sequences are products of the transcription of genomic DNA sequences and represent the gene expression process that organisms use to synthesize protein or RNA molecules. These RNA sequences can be compared between organisms of the same or different species to demonstrate similar functional proteins. There are several classes of RNA sequences (mRNA, rRNA, tRNA, ncRNA, etc.), with different biological functions. The correct identification of each class of RNA sequences is important because of the huge volume of unlabelled data available. In this context, this study proposes an approach based on the Resonant Recognition Model (RRM) for feature extraction and classification regarding the ncRNA and mRNA classes. To assess the proposed approach, it was adopted the dataset from the PLEK method. Despite the reduction of the input data size achieved using the RRM model, the results show high accuracy for primary protein sequences translated from RNA sequences, signaling the potential of the proposed approach to classify RNA.