Agriculture is one of the most important industries in the world. In this context, the importance of Brazil as a strategic country to meet a range of SDG’s targets linked to food security, fighting against hunger, and poverty reduction is undeniable. This study aimed to highlight the production and dissemination of scientific research developed by Brazilian institutions, and to identify prominent authors and institutions based on articles related to sustainability, agriculture, livestock, and agribusiness. A bibliometric analysis was developed based on a sample of 3139 documents published between 2000 and 2022, comprising 21,380 authors that were then analyzed using the Biblioshiny package. As result, the term “sustainability” showed growth as it branched out to semantically similar terms, such as “sustainable agriculture” and “sustainable intensification”; and “crop–livestock integration” and “agroforestry” were highlighted as important in the development of future research. The majority of documents were produced by the University of São Paulo (~33%), the State University of São Paulo (~15%), and the Federal University of Rio Grande do Sul (~11%), suggesting that their researchers could act as coordinators in future research through the formation of multi-collaborative groups to jointly lead to the participatory elaboration of public policies that promote more sustainable paths for agricultural production.

Background: Expression is a key skill in music performance, and one that is difficult to address in music lessons. Computational models that learn from expert performances can help providing suggestions and feedback to students. Aim: We propose and analyze an approach to modeling variations in dynamics and note onset timing for solo violin pieces with the purpose of facilitating expressive performance learning in new pieces, for which no reference performance is available. Method: The method generates phrase–level predictions based on musical score information on the assumption that expressiveness is idiomatic, and thus influenced by similar–sounding melodies. Predictions were evaluated numerically using three different datasets and against note–level machine–learning models, and also perceptually by listeners, who were presented to synthesized versions of musical excerpts, and asked to choose the most human–sounding one. Some of the presented excerpts were synthesized to reflect the variations in dynamics and timing predicted by the model, whereas others were shaped to reflect the dynamics and timing of an actual expert performance, and a third group was presented with no expressive variations. Results: surprisingly, none of the three synthesized versions was consistently selected as human–like nor preferred with statistical significance by listeners. Possible interpretations of these results include the fact that the melodies might have been impossible to interpret outside their musical context, or that expressive features that were left out of the modeling such as note articulation and vibrato are, in fact, essential to the perception of expression in violin performance. Positive feedback by some listeners toward the modeled melodies in a blind setting indicate that the modeling approach was capable of generating appropriate renditions at least for a subset of the data. Numerically, performance in phrase–level suffers a small degradation if compared to note–level, but produces predictions easier to interpret visually, thus more useful in a pedagogical setting.

Government plans are crucial for strengthening democratic regimes by defining clear policies and priorities, and by serving as essential channels of communication with voters and social groups within electoral systems. The political commitment to align these plans with the United Nations Sustainable Development Goals (SDGs) of the 2030 Agenda reflects an effort to integrate global priorities into local agendas. Such alignment is critical for fostering sustainable and inclusive progress. However, despite its importance, research on the intersection between government plans and the SDGs remains limited. In this study, we used natural language processing (NLP), artificial intelligence, and statistical analysis to examine the government plans of ten candidates in the 2022 gubernatorial election in the Brazilian state of São Paulo. Our analysis identified key policy topics, explored patterns and trends, and assessed alignment with the 17 SDGs across the social (SDGs 1, 2, 3, 4, 5, 10, and 16), environmental (SDGs 6, 13, 14, and 15), and economic (SDGs 7, 8, 9, 11, and 12) dimensions. The findings emphasize the need for government plans that are more closely aligned with sustainable development goals and that demonstrate a strong political commitment to addressing critical issues, particularly the impacts of climate change. This is especially urgent for the state of São Paulo, which faces significant challenges, including natural disasters, heat waves, flooding, water scarcity, and infrastructure deficiencies.

This paper describes Cocoruta, an open-source hub for computational linguistics resources and language models related to the Brazilian legal context, with a focus on ocean law. The Cocoruta hub consists of open-access, curated corpora and datasets, and fine-tuned language models. Cocoruta includes two sets of resources. The first set is associated with a training dataset for question-answering tasks, whereas the second set features a dataset prepared to support model training for dialogue tasks, in addition to having refined curation procedures. In this paper, we provide a comprehensive analysis of Cocoruta's contributions, including information to ensure transparency and reproducibility of Cocoruta's construction process, fine-tuning of language models, and quantitative and qualitative evaluations of models. Quantitative evaluations are based on various performance metrics, while qualitative evaluations are conducted using human assessment procedures. This work contributes by advancing fundamental resources in specialized language domains and by fostering research and development in Brazilian legal natural language processing, serving as a hub to bring together efforts in this field.

Propõe-se uma estratégia de modelagem e de análise formal de políticas de segurança para sistemas baseados em fluxos de trabalho (workflows) e contendo regras que envolvam aspectos de lógica de negócios....