Predicting road freight prices is a challenging task influenced by multiple factors. Understanding which variables have the greatest impact is essential for building more accurate models, and consequently for enhancing the competitiveness of Brazilian soybeans in the global market. This study aims to evaluate the influence of different exogenous variables on soybean freight prices and to analyze how this influence varies across different distance ranges. To achieve this, a combination of machine learning techniques was applied to a comprehensive dataset containing information on freight costs, regional characteristics, production, fuel prices, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, directly reflecting operational expenses such as fuel consumption and labor costs. Additionally, macroeconomic factors such as the exchange rate and export volume play a crucial role, highlighting the global context of Brazil’s soybean exports. Stratified analysis by distance ranges reveals distinct patterns; short-distance freight is predominantly related to domestic markets, while medium- and long-distance freight are strongly linked to export logistics.

The productivity of software developers is influenced by various factors encompassing technical, organizational, and individual aspects. Among these factors, mental health has emerged as a critical element for sustaining performance and well-being in this context. This work primary focus lies in understanding developers’ perceptions and acceptance of metrics related to mental health and productivity. Firstly, a mapping study was conducted to map the existing knowledge in this area. A total of 178 papers were retrieved using a predefined search string. After applying strict inclusion and exclusion criteria, five secondary studies were selected and 99 factors influencing productivity and/or mental health in the workplace were identified. Secondly, a metrics catalog was developed based on these influencing factors, combining established indicators from literature with new metrics designed to monitor developers’ mental health and productivity. The catalog includes 12 metrics along with their respective measurement formulas. Thirdly, an assessment of this catalog was performed through a survey and structured interviews with industry professionals, gathering insights on the applicability and relevance of the proposed metrics. The survey was completed by 47 software developers, 22 of whom also participated in the interviews. Our results reveal that while developers largely recognized the value of the catalog, resistance emerged when these metrics were positioned as evaluative mechanisms in the workplace.

While depressive linguistic expressions have been extensively studied in traditional clinical contexts, there has been comparatively little attention devoted to modeling how both depressed and non-depressed individuals express their symptoms on social networking sites as a holistic thematic process. This study addresses this gap by examining how depression is expressed linguistically on social networking sites using various topic modeling techniques, including an innovative methodology based on LLMs. We use datasets in the Brazilian Portuguese language gathered from Instagram, Reddit, and X. Our evaluation reveals that while common themes related to depression emerge across different social networking sites, each platform's unique characteristics influence the thematic content. Reddit discussions focus on symptomatology, Instagram on travel and positive emotions, and Twitter on everyday life and media. The LLM-based approach produced more interpretable topics with a higher embedding-based coherence metric, whereas traditional methods often resulted in noisy and less internally coherent topics. This research contributes to a deeper understanding of the holistic online expressions of depression and highlights the potential of advanced topic modeling techniques to reveal subtle aspects of mental health discussions online.

Este trabalho apresenta o desenvolvimento de um sistema para monitoramento de sintomas depressivos a partir de publicações em redes sociais, com foco na plataforma Reddit. Por meio da combinação de técnicas de Processamento de Linguagem Natural e modelos de aprendizado de máquina, as publicações foram classificadas quanto ao potencial indício de depressão, sendo complementadas por análises léxicas e temáticas. A aplicação web desenvolvida permite visualização estatística e monitoramento em tempo real, com funcionalidades voltadas ao apoio de profissionais de saúde mental. Os resultados obtidos evidenciam uma correlação significativa entre o conteúdo das comunidades analisadas e a presença de sinais depressivos, sugerindo o potencial do sistema como ferramenta auxiliar na triagem precoce de sintomas. Questões éticas relacionadas à privacidade e à acurácia das classificações também são discutidas.