Universidade Estadual de Londrina

X-ray fluorescence (XRF) calibration for quantitative analysis is typically performed using certified reference materials (CRMs), where the relationship between the concentration of an element and the net area of its peak in the spectrum is assumed, after properly applying corrections for possible matrix effects, to be linear. The calibration is often obtained by Ordinary Least Squares (OLS) by minimizing the vertical distance between regression line and data points taking into account the experimental uncertainty. However, uncertainties in CRM-certified concentrations are often neglected, potentially leading to underestimated errors in the calibration model. In this work, a Bayesian approach is employed to obtain calibration curves by minimizing the orthogonal distance, taking into account both experimental and certified uncertainties. Twenty-four geological, soil, and sediment CRMs were analyzed using an energy-dispersive XRF spectrometer, and the resulting spectra were employed for constructed calibration curves for eight oxides and seven elements. The model parameters were estimated via maximum a posteriori (MAP) optimization and the posterior was evaluated by a Gibbs sampling algorithm in an orthogonal parameter space. A principal components analysis (PCA) was implemented for this coordinate transformation to remove the linear correlation. Validation against independent samples shows that the Bayesian approach demonstrates that the ordinary least-squares regression calibration curve deviates from the orthogonal Bayesian MAP curve by up to one standard deviation, particularly for elements with higher reference material variability and lower concentrations. Mean absolute errors and root mean squared errors indicate improved predictive performance for major oxides, while trace elements exhibit variable agreement due to instrumental and sample-specific factors. The results demonstrate that orthogonal Bayesian XRF calibration can enhance both accuracy and uncertainty quantification in multi-elemental analyses. • Novel Bayesian calibration for XRF accounts for uncertainties in both net peak area and CRM-certified concentrations. • Significant discrepancies between Bayesian and OLS regressions are observed where CRM uncertainties are high. • MCMC sampling provides full posterior distributions for parameters, enabling credible intervals and LOD estimates.

This study aimed to evaluate the presence of antibodies to Toxoplasma gondii and Leishmania spp. in nonhuman primates and wild felines from the Municipal Zoo in the western region of the State of Paraná, Brazil. Blood collections from wild primates and felines were performed after chemical immobilization of the animals and were sent for serological diagnostics and testing for anti-T. gondii and anti-Leishmania antibodies, using the indirect immunofluorescence technique. Of the 19 primate samples, 47.37% were reactive for anti-T. gondii antibodies, and 15.79% were reactive for anti-Leishmania antibodies. Of the seven samples from wild felines, none were reactive for anti-T. gondii antibodies, and 14.28% were reactive for anti-Leishmania antibodies. The results of this work allow the knowledge of these two protozoa in wild species (nonhuman primates and wild felines), coming from rescues and seizures by different environmental agencies. This is a very favorable situation for the knowledge of the epidemiological aspects involved in the dissemination of these two zoonotic diseases in the western region of the State of Paraná. The serological results of this work make it possible to discuss the presence of antibodies possibly associated with the epidemiological characteristics of the western region of the State of Paraná.

The purpose of this article is to present the Brazilian Physical Activity (PA) During Cancer Treatment Guidelines(PA) during cancer treatment, and discuss the adaptations made to align the recommendations with the Brazilian context and its distinctive peculiarities. The development of Brazilian Guidelines for recommending PA during cancer treatment consisted of identifying international guidelines and positions and adapting their recommendations and statements for the Brazilian context. For this, an expert working group including scientists, exercise specialists, and oncologists revised the international guidelines, selected outcomes, and created recommendations based on PA safety and efficacy on the selected adverse effects of cancer treatment and the regional context. After review of 14 PA oncology guidelines, two were selected. Drawing from the selected guidelines, the working group formulated the recommendations considering the scientific evidence extracted from the documents and the Brazilian contextualization. Although the guidelines were based on international recommendations, the expert working group introduced adaptations to better reflect the Brazilian context. These adaptations included translating the content into Portuguese and simplifying the language to improve accessibility for healthcare professionals. Safety information and contraindications were added, and recommendations were adjusted not only in terms of intensity, mode, and frequency but also to promote accessibility, motivation, and long-term adherence. Additionally, the guidelines took into consideration professional regulations, access to qualified staff, the structure of the health system, and regional sociocultural and economic disparities, particularly in underserved and rural areas. There is a clear need for context-specific exercise oncology research and policies that reflect the low and middle-income countries’ social, economic, and cultural characteristics. This Brazilian guideline represents a first and essential step toward aligning Brazil with the international agenda for establishing PA standards in oncology.

O advento das redes sociais propiciou a criação do influenciador digital mirim, que, de maneira análoga, exerce virtualmente o trabalho artístico infantojuvenil, posto que há contratação e remuneração pelas atividades voltadas à divulgação de produtos e serviços, bem como a produção de conteúdos de entretenimento, a fim de atender o mercado consumidor nesse contexto. Assim, o problema da presente pesquisa consiste na verificação do resguardo do melhor interesse da criança no mundo digital quando ocorre a exploração econômica, principalmente a partir do advento do projeto de Lei nº 3.914-B de 2023 (“Lei Larissa Manoela”), considerando-se os seus direitos fundamentais e da personalidade. Dessa forma, objetiva-se de modo geral analisar a gestão e proteção dos recursos oriundos dessa atividade, incumbida aos responsáveis legais frente ao ordenamento jurídico brasileiro. Especificamente, buscou-se analisar a evolução histórica e o arcabouço normativo do trabalho infantojuvenil no Brasil, com foco nas atividades artísticas e intelectuais. Por fim, abordou-se as inovações propostas pelo Projeto de Lei nº 3.914-B/2023, visando a tutela jurídica e a gestão patrimonial dos proventos auferidos por crianças e adolescentes. A fim de atendê-los, adotou-se o método dedutivo, explorado qualitativamente, isto é, com o uso de descrições, conceitos e análises de legislações vigentes juntamente a fenômenos da Era Digital. Para a concretização dos métodos foi adotada a pesquisa documental indireta, com a utilização de obras literárias, doutrinas, artigos científicos, sites, entre outros.

Federated learning (FL) enables the training of machine learning models on decentralized data, potentially improving data privacy. However, the FL distributed architecture is vulnerable to poisoning attacks. In this paper, we propose an FL method capable of mitigating these attacks through a triad of defense strategies: organizing clients into groups, evaluating the local performance of global models during training, and using a voting scheme during the inference phase. The proposed approach first divides the clients into randomly sampled groups, each generating a distinct global model. Each client trains a local model on their private data and submits it to the central server. The central server aggregates the local models within each group to generate the global models. Then, each client receives all global models, selects the best performing one as their new local model, and the process repeats until training is complete. During the inference phase, each client classifies its inputs according to a majority-based voting scheme among the global models. Our experiments using the HAR and MNIST datasets demonstrate that our method can effectively mitigate poisoning attacks without compromising the global model's performance.

The evaluation of soil samples using spectral techniques provides a sustainable approach to soil health assessment, reducing reliance on traditional, waste-producing analytical methods. Over the past few decades, spectroscopic and spectrometric techniques have gained prominence in soil analysis due to their non-destructive nature. X-ray fluorescence (XRF) and mid-infrared spectroscopy (MIRS) are particularly valuable, as they provide complementary information on the elemental and molecular composition of soils, respectively. Both have been successfully combined with machine learning algorithms to model and predict soil fertility parameters as alternatives to conventional wet chemistry. This study explores the potential of data fusion between XRF and MIRS measurements to enhance soil fertility prediction accuracy. A total of 160 soil samples were analyzed using a Panalytical Epsilon 5 EDXRF spectrometer, employing four different secondary targets, and a Bruker Alpha II Fourier-transform mid-infrared spectrometer. Three machine learning models were trained on individual and fused datasets: Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Random Forest Regression (RF). The regression models built upon the fused data yielded increased performance for organic Carbon (OC), exchangeable Calcium (exCa), and exchangeable Potassium (exK). For OC, the RF model yielded the best performance, with the fused approach achieving a 5% reduction in RMSE and a 7% increase in RPD relative to standalone XRF. For exCa, RF was again the top-performing algorithm under fusion, providing a 25% reduction in RMSE and a 51% increase in RPD. For exK, the best results were obtained with PLS, which delivered a 16% reduction in RMSE and a 27% increase in RPD. These results demonstrate that integrating complementary spectral information from XRF and MIRS can enhance the prediction of key soil fertility attributes, offering a reliable and sustainable alternative to conventional chemical analyses. Beyond improving model accuracy, the proposed fusion framework highlights the potential of combining multi-sensor data to expand the applicability of spectral techniques for large-scale, rapid soil fertility assessment.