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 paper 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.

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One of the key aspects of Industry 4.0 is using intelligent systems to optimize manufacturing processes by improving productivity and reducing costs. These systems have greatly impacted in different areas, such as demand prediction and quality assessment. However, the prognostics and health management of industrial equipment is one of the areas with greater potential. This paper presents a comparative analysis of deep learning architectures applied to the prediction of the remaining useful life (RUL) on public real industrial datasets. The analysis includes some of the most commonly employed recurrent neural network variations and a novel approach based on a hybrid architecture using transformers. Moreover, we apply explainability techniques to provide comprehensive insights into the model's decision-making process. The contributions of the work are: (1) a novel transformer-based architecture for RUL prediction that outperforms traditional recurrent neural networks; (2) a detailed description of the design strategies used to construct the models on two under-explored datasets; (3) the use of explainability techniques to understand the feature importance and to explain the model's prediction and (4) making models built for reproducibility available to other researchers.

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