Abstract
Artificial Intelligence (AI) is transforming how humans communicate, create, and connect. As algorithms increasingly mediate interactions, they shape not only the content of human expression but also the dynamics of relationship building. There is a growing need for research that examines the profound implications of AI for creativity, emotional exchange, and social bonds, exploring both the opportunities it offers and the challenges it presents.

Abstract
Architectural uncertainties arising from incomplete or unclear information pose significant challenges when making architectural decisions in Agile teams. Based on a limited number of case studies that employed a technique called ArchHypo, four patterns were identified that propose small adjustments in the development process to handle architectural uncertainties: PROTECTIVE GUIDELINE, BRING THE SPECIALIST, PLAN FOR PREPARATION, and QUALITY CHECKPOINT. Although the patterns derived from these experiences can be useful in real projects, their applicability and consequences were based on limited evidence and specific scenarios. To address this issue, this paper presents an interview study with experienced software architects and engineers to gather further information on the application of these patterns. The research method employed semi-structured interviews to gather the experiences of professionals with the target practices, and thematic analysis was used to assess their recurrence, applicability, and consequences. The findings confirmed that most professionals recognized those practices in real projects and their suitability as actions in uncertainty management. Moreover, new positive and negative consequences, not previously documented in the patterns, were identified. As a result, this work contributes to the field by providing guidance to professionals on how to better evaluate the trade-offs of those patterns when applied to architecture uncertainty management.

Abstract

Abstract
The growing availability of online data creates new opportunities to improve the timeliness and detail of official statistics, particularly in domains such as price monitoring and inflation measurement. However, leveraging web-scraped data for official use requires alignment with standardized classification frameworks such as the European Classification of Individual Consumption According to Purpose (ECOICOP). We train two natural-language models, a lightweight convolutional neural network (CNN) and a fine-tuned BERTimbau transformer, to classify Portuguese food and beverage items into ECOICOP categories. Using 100,000 product titles scraped from six national supermarket sites and labeled via a human-in-the-loop workflow, the CNN reaches a macro-F1 of 92.19 % with minimal computing cost, while the transformer attains 94.00 %, the first such result for Portuguese. Both models are published on Hugging Face, enabling reproducible inference at scale while the source data remain confidential. The study delivers the first open-source Portuguese ECOICOP classifiers for food and beverage products, a replicable low-resource labeling workflow, and a benchmark of accuracy-speed trade-offs to guide researchers in similar tasks.

Abstract
This study aimed to develop a strategic marketing plan for the Polytechnic University of Viana do Castelo. The methodology applied was a questionnaire survey applied in person to the students of the Polytechnic University of Viana do Castelo, during the class period. A total of 1762 valid responses were obtained. Although some answers indicate the necessary improvements to be made, most respondents are satisfied with the UPVC, pointing it out as young, welcoming and accessible higher education institution.

Abstract