One of the most used power converters is the three-phase voltage source power inverter. This is one of the reasons why it is so important to assure a continuous and safety operation of this power converter. This paper considers the application of PCA and average value computation to detect and identify faulty power switches in voltage source inverters. The PCA is applied to the inverter output currents in order to get a specific pattern that allows the identification of the fault. This results in a fast and reliable fault detection methodology. The applicability of the proposed technique is confirmed through simulation and experimental results.

This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this work are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; and (ii) implementation of a proof-of-concept, named SENSE (Semantic Enrichment knowledge Sources).

Climate variability directly influences agriculture, especially in a scenario of global change and transition to a sustainable bioeconomy. This study analyzed historical series (1994–2023) of productivity and harvested area of annual crops (corn, cassava, and beans) and perennial crops (pineapple, cocoa, annatto, avocado, and guava), in order to understand the relationship between rainfall, maximum temperature, and agricultural production in northern Brazil. To achieve this, the Augmented Dickey–Fuller (ADF) test was applied to verify the stationarity of the series, and principal component analysis (PCA) was used to identify correlation patterns between climate and production variables. The ADF test showed that annual precipitation is stationary, while maximum temperature is non-stationary, confirming a warming trend. Among the crops, only bean productivity was stationary, albeit at low levels, while corn, cassava, and cocoa showed non-stationary behavior, reflecting technological advances combined with climatic pressures. PCA indicated different responses: corn showed a positive association with temperature, but signs of recent stagnation, whereas cassava and beans depended more on precipitation, demonstrating vulnerability to drought. Among perennials, avocado and guava responded positively to increased temperature, while annatto and pineapple were more dependent on rainfall. Cocoa showed a balanced correlation with both variables. It can be concluded that climate impacts on agriculture are heterogeneous and require specific adaptive strategies. From a bioeconomy perspective, the importance of productive diversification, technological innovation, and public policies aimed at climate resilience and the sustainability of low-carbon value chains is highlighted.