Viviane Rodrigues Botelho

O câncer de mama é um dos cânceres mais comuns em todo o mundo. Avanços recentes no diagnóstico precoce e tratamento resultaram em uma alta probabilidade de sobrevivência do paciente. A radioterapia tem sido um tratamento crucial para casos de câncer de mama. Ao mesmo tempo, uma análise da distribuição de dose no corpo do paciente é necessária. Neste artigo, um tratamento de radioterapia para câncer de mama foi simulado usando o código de transporte de radiação PENELOPE e o phantom mamário PPMA. Parâmetros dosimétricos foram determinados para a configuração de irradiação nas três metodologias diferentes. As simulações foram realizadas em um equipamento LINAC Varian 600CD operado com um campo aberto a uma energia de 6 MV. Concluiu-se que as três condições de irradiação correlacionaram-se em 98,92%, de acordo com a análise gama realizada. A ferramenta computacional apresentada neste artigo pode ser utilizada para comparações de distribuições de dose em serviços de radioterapia que não possuem phantom específicos.

High‐frequency ultrasound (HFUS) is valuable for assessing skin lesions, supporting diagnosis, treatment monitoring, and surgical planning. This study evaluates deep learning models for binary classification of HFUS images acquired in B‐mode and Doppler mode. Two single‐input CNNs were trained with each modality, while Unity and Cascade architectures combined both. The HFUS‐Doppler model achieved the best performance (95.0% accuracy, AUC 0.98), followed by Unity (90.5% accuracy, AUC 0.97). Cascade showed lower accuracy but greater confidence in malignant predictions. Probability distribution analysis revealed differences in model certainty near the decision threshold. Results indicate that combining B‐mode and Doppler can enhance diagnostic performance, depending on network design and data quality, supporting the potential of customized deep learning for non‐invasive HFUS‐based skin lesion classification.

Este artigo traz uma Revisão Sistemática da Literatura explorando a integração entre Game Learning Analytics (GLA) e Sistemas de Recomendação (SR), analisando os estudos primários localizados nas principais bases de dados de literatura científica e revistas nacionais. A metodologia utilizou um framework de 6 etapas, iniciando com a definição do tema central até a apresentação dos resultados da pesquisa. A análise final apresentou um número reduzido de seis artigos encontrados, sendo plausível afirmar que há uma lacuna em estudos sobre o uso de GLA integrado a SR. Desse modo, existe oportunidade para desenvolvimento científico na coleta de dados dos jogos digitais e o uso de SR em novas propostas de pesquisa em informática na educação no âmbito nacional e internacional.

Objective: AQMI - “Assessment of the quality of mammographic images” was developed to support the quality control (QC) of digital mammographic images. Materials and Methods: The software was implemented in the Python programming language via the Streamlit library, which involved content structuring and environmental planning. The experimental data that were selected from a public domain repository [19]. From the selected database, relevant information that was present in the DICOM file was studied to perform the image quality test. Then, from searching the literature, indicators that measure image quality were found, such as the signal-to-noise ratio, the contrast-to-noise ratio, figure of merit and image histogram. Results: AQMI assists in analyzing the image quality test established in IN 92 by the Agência Nacional de Vigilância Sanitária [8]. It also has quality addition indicators, trend graphs, and the image assessment history. Conclusion: For the functionalities of this work, the developed software is a promising tool for use in clinical practice, since it consists of a free, friendly, and easy-to-use interface.

Grabbing students' attention is becoming more challenging in the digital age, distance education, and increasing low literacy. This complex educational phenomenon needs to be mitigated to prevent negative social and economic consequences. This work presents the use of gamification in higher education to engage and motivate students in Calculus, a course that traditionally presents high failure and dropout rates. The main goal is to report an experience developing an RPG-style educational digital game to be used and tested in part of the course during one semester. The project applied concepts of gamification, educational digital games, digital game-based learning, and the theory of meaningful learning as a theoretical basis for the formulation of the game. The results showed positive acceptance of this approach by the students and an increase of interest in proceeding with using the game for learning purposes. According to the results, 73% of students declared the game contributes to the learning process, and 82% showed interest in using the game for the whole semester.

The longevity of each MPC application is strongly related to its performance maintenance. This work provides an overview of the methodologies available to fulfill this task, including a discussion about some special requirements of performance assessment methodologies for typical industrial MPC applications. The available methodologies were compared using these requirements. The best approaches were selected and compared to a new method proposed by the authors. These techniques have been applied in two case studies: the Shell benchmark process and the quadruple‐tank process. The results show that the control policy (setpoint, soft constraints, targets) followed in the MPC application should be the determining factor in selecting the methodology for performance assessment.