The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitoring more intelligent, several machine learning techniques have been proposed to reduce the dimension of the input data and also to analyze it. This paper, therefore, aims to compare the use of vibration features extracted based on machine learning models, expert domain, and other signal processing approaches for identifying bearing faults (anomalies) using machine learning (ML)—in addition to verifying the possibility of reducing the number of monitored features, and consequently the behavior of the model when working with reduced dimensionality of the input data. As vibration analysis is one of the predictive techniques that present better results in the monitoring of rotating machinery, vibration signals from an experimental bearing dataset were used. The proposed features were used as input to an unsupervised anomaly detection model (Isolation Forest) to identify bearing fault. Through the study, it is possible to verify how the ML model behaves in view of the different possibilities of input features used, and their influences on the final result in addition to the possibility of reducing the number of features that are usually monitored by reducing the dimension. In addition to increasing the accuracy of the model when extracting correct features for the application under study, the reduction in dimensionality allows the specialist to monitor in a compact way the various features collected on the equipment.

This study examines the evolution of the scientific literature on mining and heavy metals, with a particular focus on biosanitary risks associated with childhood exposure. The research integrates a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, combined with a bibliometric analysis of Scopus-indexed publications, international epidemiological data, and an evaluation of the socio-environmental context in Ecuadorian mining regions. The PRISMA-based screening process was applied to identify, filter, and select relevant peer-reviewed studies, enabling the delimitation of a focused corpus of literature, with particular attention given to scientific contributions produced by Latin American researchers and institutions. The results reveal a significant concentration of knowledge production among a limited number of countries and institutions, the dominance of English as the main language of scientific communication, and the centrality of journals in environmental sciences and toxicology. While notable progress has been made in identifying contaminants and exposure pathways, governance structures, territorial disparities, and policy implementation processes remain insufficiently explored. In Ecuador, the rapid growth of mining concessions in ecologically sensitive zones presents potential threats to children’s neurocognitive development, highlighting the urgent need for ongoing surveillance, biomonitoring programs, and preventive public health measures. The study emphasizes the importance of strengthening regional research capacity and fostering more equitable international scientific collaborations to ensure that knowledge production is responsive to local contexts and effectively safeguards vulnerable populations.

Iridology is an alternative diagnostic method that assesses overall health by examining the iris. Proponents claim that each organ in the body is linked to the iris via nerve connections, with dysfunctions manifesting as distinctive markings on the iris. While iridology has been relegated to pseudoscience in Western medicine, it has been of particular interest in Eastern health practices for over a millennia. With advancements in deep learning, there is renewed interest in iridology as a health assessment tool. Deep learning algorithms can analyze iris images objectively, potentially validating iridology's claims. This study explores using YOLOv8, a deep learning model, to detect tension rings in iris images as an indicator of anxiety. By training the model on a labeled dataset of iris images, we aim to develop a non-invasive, cost-effective tool for anxiety assessment. Results show that YOLOv8 can reliably detect tension rings, suggesting its potential as a diagnostic aid.

Diversas empresas utilizam o gás liquefeito de petróleo (GLP), comumente conhecido como “gás de cozinha” para realizar o aquecimento da água dos chuveiros de vestiários. O uso do GLP para esta finalidade representa um custo elevado, no entanto, empresas que possuem um sistema de produção de ar comprimido poderiam recuperar o calor gerado no processo de compressão do ar atmosférico e utilizá-lo como substituinte do GLP para o aquecimento da água, reduzindo custos e impactos ambientais. Neste sentido, objetivou-se analisar a viabilidade econômica e ambiental da recuperação do calor gerado na produção de ar comprimido em substituição ao GLP para o aquecimento da água dos chuveiros em uma indústria química no estado de Minas Gerais. Incialmente foram orçados os custos do consumo de GLP para o aquecimento dos chuveiros e da implantação do projeto de recuperação do calor produzido pelos compressores. Os resultados deste estudo de viabilidade apontaram que a implantação deste tipo sistema de recuperação de calor produzido durante a compressão do ar comprimido é economicamente viável. Além disso, esta substituição promoveria o aumento da eficiência energética global desta indústria, bem como uma redução da emissão de CO2. Esta análise também demonstrou que a inclusão deste tipo de instalação de recuperação de calor no projeto inicial pode influenciar na localização dos compressores, reduzindo os gastos com isolamento térmico e tubulações e aumentando a eficiência da recuperação do calor produzido.

Anais do Congresso Internacional de Engenharia Mecânica e Industrial (978-65-5941-094-1) - Aplicação De Elementos Finitos Na Avaliação Da Integridade Estrutural Da Viga Principal De Uma Retomadora De Minério De Fosfato