Abstract
Early detection of gastrointestinal lesions such as intestinal metaplasia (IM), dysplasia, and polyps remains challenging due to their subtle appearance and the scarcity of well-annotated medical image datasets. To address this limitation, we introduce Cut Instance Mixing (CIM), a domain-specific data augmentation method designed to generate anatomically plausible lesion-containing images through the identification of biologically relevant regions of interest and seamless lesion blending using Poisson image editing and gradient-based mixing. CIM was evaluated across three distinct endoscopic datasets (IM, dysplasia, and polyps) using a ResNet50 classifier and five-fold cross-validation. The proposed method consistently outperformed state-of-the-art augmentation techniques. In IM classification, CIM with alpha = 0.8 achieved the highest performance (AUC: 0.879, Accuracy: 0.823), surpassing MixUp, CutMix and random copy-paste. In dysplasia detection, CIM reached near-perfect results (AUC: 0.997, Accuracy: 0.966), and demonstrated strong generalization on an external polyp dataset (AUC: 0.830, Accuracy: 0.769). Grad-CAM analyses further confirmed that CIM preserves clinically relevant features, improving model attention on lesion regions. These findings demonstrate that CIM enables the generation of realistic and biologically coherent synthetic samples, effectively mitigating data imbalance and enhancing classification robustness. The method is architecture-agnostic and broadly applicable to tasks requiring anatomically consistent augmentation, providing a promising direction for improving deep learning systems in gastrointestinal imaging.

Abstract
This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R2 from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations.

Abstract
Disinformation is an ancient social phenomenon that has found a favourable environment for dissemination in internet-based social networks. While the scientific community seeks to address the problem by creating specific tools to detect and classify the various types of false information, we argue that systems thinking is necessary to understand and holistically address this major threat. The works that directly cite Disinformation Systems treat this term as a grouping of concepts, mechanisms, objectives and institutions in a large multidisciplinary repository that finds a self-explanation in the term systems. Through a qualitative and theoretical basis, this research proposes that the generation of disinformation can be defined as a system model, theorizing that the entire process of creating, producing and disseminating disinformation can be defined systematically. Thus, we define an initial descriptive model and affirm that the generation of disinformation can be characterized in terms of a sociotechnical work system. We tested the model in historical disinformation scenarios showing that it fits the components and flows of the system. Although initial, this work has the potential to enable the development of new systemic insights and research in the area of disinformation.

Abstract
Disinformation, although an ancient phenomenon, has gained unprecedented reach and speed with the rise of the internet and social media platforms. While traditional fact-checking approaches focus on the semantic content of information, this article proposes a quantitative analysis based on metadata and formal textual features to investigate disinformation from a quality dimension perspective, assuming that false or misleading information often fails to meet informational quality criteria. Using an experimental approach, we analyzed two datasets of news from reliable and unreliable sources and applied statistical methods, including the Mann–Whitney U test, Cliff’s Delta, and Rosenthal’s r, to measure differences and effect size in the quality dimensions of accuracy, currency, readability, consistency, and reliability. The results show that lexical cohesion and lexical diversity are the strongest discriminators of source reliability, followed by structural error rates, while currency and readability display only weak discriminative power. The proposed News Reliability Index (NRI) emerges as a moderate but complementary indicator. Overall, reliable sources consistently demonstrate higher information quality, but structural differences alone are insufficient to detect disinformation, especially considering the capacity of generative AI to produce syntactically coherent texts. We conclude that semantic content analysis remains essential for identifying disinformation, with structural features best applied as supporting signals in detection models. Finally, we highlight future challenges, such as the growing use of artificial intelligence in generating high-quality disinformation, which may reduce the effectiveness of structural metrics and complicate automation in verification processes. © 2026 Copyright held by the owner/author(s).

Abstract

Abstract
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and one of the leading causes of blindness worldwide. It is characterized by the appearance of lesions on the retina, such as microaneurysms, hemorrhages, hard exudates, and soft exudates, which are crucial for staging the disease. Diagnosis is typically performed through analysis of fundus images, a manual process that is time-consuming and prone to subjectivity. To address this, this study explores the automatic segmentation of DRrelated lesions using deep learning techniques. Four convolutional neural network architectures were evaluated: U-Net, FPN, DeepLabV3+, and Attention U-Net. The IDRiD dataset was used for training and validation The DeepLabV3+ model with ResNet50 achieved the highest overall performance, while FPN was the only model capable of detecting microaneurysms in the multiclass task. These findings underscore the importance of architecture selection, loss function design, and preprocessing choices. Future work may explore new datasets, enhanced data augmentation, and the impact of optic disc removal on segmentation accuracy. © 2025 The Authors. Published by Elsevier B.V.