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Dimensionamento da pasteurização para a obtenção de um caldo de cana destinado à produção de frozen de cana: um estudo de caso
(Universidade Federal de São Carlos, 2025-06-04) Oka, Yasmin Naomi Gushiken; Neves, Isabelle Cristina Oliveira; https://lattes.cnpq.br/3479449098249392; Furtado, Guilherme de Figueiredo; https://lattes.cnpq.br/6217162662863550; https://lattes.cnpq.br/4807028813366321
Sugarcane juice, also known as "garapa," is a traditional beverage widely consumed in Brazil, especially at open-air markets, and is obtained by extracting the liquid from sugarcane stalks. Aiming to diversify its consumption, a startup developed the concept of frozen sugarcane juice, in which the freshly extracted juice is immediately frozen to extend its shelf life. However, this process does not fully ensure the product’s quality and safety, and it requires thawing before being processed in the frozen beverage equipment. In this context, the present study aimed to evaluate the feasibility of applying pasteurization as an alternative to freezing, allowing shelf life extension, while maintaining microbiological safety and improving process efficiency. To achieve this, a fictional heat exchanger was designed and dimensioned for the pasteurization of raw sugarcane juice, using thermodynamic calculations and physical-chemical parameters. The proposed plate heat exchanger demonstrated thermal efficiencies ranging from 77% to 81%, values consistent with technical references. It is concluded that the adoption of pasteurization, supported by the use of plate heat exchangers, presents a promising alternative for the sugarcane juice processing industry, offering benefits in both food safety and operational cost reduction.
Análise de fotocondução e efeito de memória resistiva em filmes finos de ZnO dopados com sódio
(Universidade Federal de São Carlos, 2025-06-04) Magalhães, Bruno Trefilio; Godoy, Marcio Peron Franco de; https://lattes.cnpq.br/2609654784637427; https://orcid.org/0000-0002-8376-8642; https://lattes.cnpq.br/7547498992247322
With the increasing use of computational resources in everyday life, the continuous search for materials that can lead to advances in electronic devices and computing in general is necessary, whether in terms of computational or energy efficiency. In particular, zinc oxide (ZnO) presents favorable properties for applications in both photonics and the development of memristors. The investigation of the operating and performance characteristics of materials with memristive properties can improve the way we store information, and new computational architectures can be established, such as neuromorphic computing. In this work, the photoconduction characteristics and the memristive effect under the influence of ultraviolet light and different atmospheres on a thin film of 𝑍𝑛𝑂 doped with sodium (ZnO:Na) are investigated. The photoconductivity of the film was evaluated in several atmospheres - argon, synthetic air, free atmosphere, nitrogen, oxygen, carbon dioxide and vacuum - as well as the role of different light stimuli in photoconduction. In the absence of oxygen, the persistence of photoconductivity (PPC) phenomenon is observed more intensely in vacuum and carbon dioxide, with half-lives longer than one hour. The presence of oxygen is destructive to the conductivity of the film, impairing the retention of long-term memory (LTM). For the memristive effect, voltage versus current scans (I-V scans) were performed on the sample immersed in dry atmospheres (synthetic air and carbon dioxide) and in a free atmosphere. Lighting enhances the memristive effect, as seen in the larger internal areas when illuminated: 219𝜇A in synthetic air, 147𝜇A in a free atmosphere and 17𝜇A in carbon dioxide, compared to initial areas of around 0.1𝜇A. The memristive characteristics of the film are not altered by lighting but by the atmosphere, reinforcing that memristance is a surface effect.
Desenvolvimento e avaliação de um programa de habilidades cognitivas e emocionais para promoção do equilíbrio trabalho-vida pessoal
(Universidade Federal de São Carlos, 2025-06-04) Santos, Gabriela Trombeta; Barham, Elizabeth Joan; https://lattes.cnpq.br/9868595523538592; https://orcid.org/0000-0002-7270-4918; https://lattes.cnpq.br/5875147172625110; https://orcid.org/0000-0001-5485-1239
Many workers struggle to conciliate paid work demands with the rest of their lives, leading to negative effects on their health and well-being. However, there are few studies evaluating the effectiveness of intervention programs to improve work-life balance, which makes it difficult for professionals, organizations and public policy makers to implement evidence-based interventions. The aims of this research were to: (a) develop an intervention program on work-life balance – "Caminho do Equilíbrio Trabalho-Vida", for use via a cell phone app, and (b) evaluate the usability, acceptability, and effects of this program. The program involves a sequential learning experience covering: (a) self-monitoring, (b) psychoeducation on topics related to balancing work and personal life demands, (c) activities to increase specific cognitive and emotional skills through mindfulness training, and (d) written activities to stimulate the analysis and development of plans for improving the fit between work and personal life involvements. First, the process of developing the program and the cell phone app is presented, including a systematic literature review on the effects of mindfulness programs on work-life balance. Second, the process of evaluating this program is described, based on two pilot studies and a randomized controlled trial (RCT). The RCT was conducted with 474 Brazilian workers over the age of 18, with access to Android or iOS cell phones. Participants were randomly assigned to the intervention group (who received the program via the app) or the control group. Although retention rates were low in both groups, there were statistically significant effects indicating gains, with moderate to large effect sizes, for the group using the app, compared to the control group. These gains included increases in: mindfulness (p < 0.001, d = 1.07), satisfaction with work-life balance (p < 0.001, d = 0.94), psychological detachment from work during off-work hours (p = 0.014, rb = 0.32), satisfaction with activities for relaxation (p = 0.003, rb = 0.38), satisfaction with work productivity (p = 0.002, d = 0.72), and levels of well-being (p = 0.019, d = 0.69), as well as a significant reduction of work-to-life interference (p < 0.001, d = 0.95). No significant differences were found between the groups concerning their perceptions of life-to-work interference or of the benefits of combining work-life involvements. Six weeks after completing the program, findings for the app group were still significantly better than on the pre-test, indicating that program benefits were maintained. The main contributions of this research include: (a) an analysis of the literature on the effectiveness of mindfulness-based programs for work-life balance, and the development of a model of the relationship between mindfulness and perceptions of balance, important to support the work of professionals in this area, (b) a methodological contribution involving the creation of a mindfulness program integrated with issues of work-life balance, and (c) the evaluation of the effects of this program, indicating its potential to promote the well-being of Brazilian workers.
Agrupamento profundo de grafos usando redes neurais de grafos e seleção de sementes
(Universidade Federal de São Carlos, 2025-06-04) Lopes Filho, Carlos Pereira; Valejo, Alan Demétrius Baria; https://lattes.cnpq.br/9546164790189830; https://orcid.org/0000-0002-9046-9499; https://lattes.cnpq.br/4073822253624796; https://orcid.org/0009-0005-6290-6558; Naldi, Murilo Coelho; Marcacini, Ricardo Marcondes; https://lattes.cnpq.br/0573662728816861; https://lattes.cnpq.br/3272611282260295; https://orcid.org/0000-0002-3107-8236; https://orcid.org/0000-0002-2309-3487
Clustering plays a fundamental role in attributed graphs, which incorporate both topological structure and node attributes represented as feature vectors. Deep clustering methods based on Graph Neural Networks (GNNs) have proven effective in extracting patterns from such data. Most existing approaches use a traditional clustering algorithm to identify representative elements, which are later employed in the training of the GNN and the clustering task. However, when selecting representative elements, these clustering algorithms consider only the feature vector of each instance, neglecting topological information. This limitation negatively impacts the GNN learning process. To address this issue, we propose Deep Graph Clustering via Graph Neural Network and Seed Selection (DGCSS), a model consisting of three modules: (1) the seed selection module, which iden- tifies representative nodes; (2) the embedding module, which employs a graph attentional network to capture global topological information; and (3) the self-supervised module, which leverages the representative nodes to guide the clustering task. An advantage of our algorithm is that it integrates both the topological structure and node attributes across all modules to identify representative elements. This is the first GNN-based clustering algorithm that incorporates seed selection, establishing a significant reference for future research. The empirical analysis of real world graphs provides evidence that the use of seeds is competitive when compared to traditional algorithms, such as K-Means combined with GNNs.