User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users’ interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website.

Understanding how climate variability is reflected in streamflow is essential for reservoir management and hydropower planning. This study investigated how temporal scale influences climate–streamflow relationships, persistence characteristics, and predictability in two Brazilian reservoirs: Três Marias (São Francisco Basin) and Serra da Mesa (Tocantins Basin). Monthly streamflow and climate-index records (Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Antarctic Oscillation (AAO)) from 1979–2020 were analyzed using a 12-month moving average (MA12) filter to emphasize low-frequency variability. Temporal filtering strengthened climate–streamflow relationships, particularly for PDO and AAO, revealing signals that were less apparent in the original monthly series. Lagged-correlation analyses identified contrasting persistence structures between the reservoirs. Três Marias exhibited multi-year persistence timescales (22–27 months), whereas Serra da Mesa showed shorter and more heterogeneous response timescales, ranging from an immediate PDO response to approximately 14–19 months for ENSO and AAO. Forecasting experiments using benchmark models (Persistence and Linear Regression) and deep learning architectures (LSTM and TCN) showed limited predictive skill on the raw monthly series but substantially improved performance after temporal filtering. For the MA12-filtered series, the benchmark models achieved the highest performance in both reservoirs ($R^2\approx 0.95$ in Três Marias and $R^2\approx 0.93$ in Serra da Mesa). Overall, the results indicate that temporal scale strongly influences the detectability of climate signals, the persistence of streamflow variability, and the predictability of reservoir inflows.

Natural Language Processing (NLP) techniques are increasingly powerful for interpreting a person’s feelings and reaction to a product or service. Sentiment analysis has become a fundamental tool for this interpretation, and it has studies in languages other than English. This type of application is uncommon and unheard of in Portuguese. This article presents a sentiment analysis classification based on Portuguese social media comments. Representation of word embeddings with both pre-trained Glove and Word2Vec models were generated through a corpus entirely in Portuguese. This article presents a set of results with different models of pre-trained layers and deep learning models exclusive to the Portuguese language on social networks. Two classification models were used and compared: (i) Bidirectional Long Short-Term Memory (BI-LSTM) and (ii) Bidirectional Gated Recurrent Unit (BI-GRU), achieving accuracy results of 99.1

Since 1995, the Brazilian government has regulated a differentiated model for selling electricity with the objective of stimulating free competition. Widespread in recent years, with less restrictive adhesion requirements, the so-called free market allows the consumer to customize the contract, contemplating price, demand and supply period, established in a flexible way. Thus, the customer has the freedom to negotiate according to his needs, weighing costs and benefits. This paper describes the migration process from the Regulated Contracting Environment (RCE) to the Free Contracting Environment (FCE) and the savings achieved by a consumer of subgroup A4 and green tariff mode in the concession area of Light, which operates in the state of Rio de Janeiro. Statistical methods and artificial neural networks were employed in forecasting the consumption series, in the period from 2016 to 2019, to identify the best contracting conditions. Specifically, the long and short-term memory networks (LSTM) obtained the best performance with validation error of less than 1%. With simulated RCE data, we show the financial advantage consumers would have if they had not yet migrated to the FCE.