Abstract
This work proposes the use of a deep learning-based adversarial diffusion model to address the translation of contrast-enhanced from non-contrast-enhanced computed tomography (CT) images of the heart. The study overcomes challenges in medical image translation by combining concepts from generative adversarial networks (GANs) and diffusion models. Results were evaluated using the Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) to demonstrate the model's effectiveness in generating contrast images while preserving quality and visual similarity. Despite successes, Root Mean Square Error (RMSE) analysis indicates persistent challenges, highlighting the need for continuous improvements. The intersection of GANs and diffusion models promises future advancements, significantly contributing to clinical practice. The table compares CyTran, CycleGAN, and Pix2Pix networks with the proposed model, indicating directions for improvement.

Abstract
Static source code analysis techniques are gaining relevance in automated assessment of programming assignments as they can provide less rigorous evaluation and more comprehensive and formative feedback. These techniques focus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically represented internally as graphs, after parsing and other preprocessing stages. Static automated assessment techniques, therefore, draw inferences from intermediate representations to determine the correctness of a solution and derive feedback. Consequently, achieving the most effective semantic graph representation of source code for the specific task is critical, impacting both techniques' accuracy, outcome, and execution time. This paper aims to provide a thorough comparison of the most widespread semantic graph representations for the automated assessment of programming assignments, including usage examples, facets, and costs for each of these representations. A benchmark has been conducted to assess their cost using the Abstract Syntax Tree (AST) as a baseline. The results demonstrate that the Code Property Graph (CPG) is the most feature -rich representation, but also the largest and most space -consuming (about 33% more than AST).

Abstract
Introduction/Objective: the objective of this article is to analyze the current academic literature on smart cities in Brazil with evidence of the application of Digital Twin or Digital Shadow technology. Method: Integrative Literature Review was used as the research instrument, analyzing in the articles: a) objective; b) research method; c) study subject (location); d) application of Digital Twin or Digital Shadow; e) Results and conclusions. Results: portfolio with 25 articles on the topic and qualitative analysis regarding objective, method, study location, Digital Twin technology, Digital Shadow, and results. Studies with elements of Digital Shadow are perceived timidly in two cases of smart cities in Brazil. Conclusions: smart city technologies should be centered on the interests of users to not lose their humanity. It is worth adding that people's needs change and, therefore, smart technologies should have a forward-looking vision to anticipate the needs of future generations. Digital Twin technology is a model that can contribute in this sense, monitoring and providing readings of future scenarios for smart cities.

Abstract

Abstract
This research examined factors that help microfinance achieve sustained poverty reduction based on a systematic literature review (SLR). A search was conducted on the SCOPUS database up to December 2023. After analyzing hundreds of documents, a subset of 30 articles was subject to in-depth analysis, exploring factors and corresponding measurement indicators for sustainable poverty reduction in microfinance contexts. This article emphasizes that sustained poverty reduction is a gradual process requiring ongoing efforts from both Microfinance Institutions (MFIs) and governments. Two key success factors are empowering borrowers and ensuring the microfinance programs themselves are profitable. When implemented in an integrated and coordinated manner, these factors can empower individuals to escape poverty by fostering self-employment and income generation, ultimately reducing dependence on external support. Additionally, the study highlights the role of personality traits in influencing long-term entrepreneurial success. The findings provide valuable tools for MFIs and policymakers. MFIs gain a practical framework to guide their interventions towards sustained poverty reduction. Policymakers can leverage the identified factors and indicators when designing and implementing microfinance policies with a long-term focus on poverty alleviation. This study breaks new ground by presenting an operational framework that categorizes and integrates two critical factor groups: empowerment and beneficiary profitability. Furthermore, it links these factors to corresponding measurement indicators within a unified framework, enabling a more holistic assessment of poverty reduction efforts. © 2024 by the authors.

Abstract
Deep-learning techniques can improve the efficiency of medical diagnosis while challenging human experts’ accuracy. However, the rationale behind these classifier’s decisions is largely opaque, which is dangerous in sensitive applications such as healthcare. Case-based explanations explain the decision process behind these mechanisms by exemplifying similar cases using previous studies from other patients. Yet, these may contain personally identifiable information, which makes them impossible to share without violating patients’ privacy rights. Previous works have used GANs to generate anonymous case-based explanations, which had limited visual quality. We solve this issue by employing a latent diffusion model in a three-step procedure: generating a catalogue of synthetic images, removing the images that closely resemble existing patients, and using this anonymous catalogue during an explanation retrieval process. We evaluate the proposed method on the MIMIC-CXR-JPG dataset and achieve explanations that simultaneously have high visual quality, are anonymous, and retain their explanatory value.