https://repositorio.ufu.br/handle/123456789/18792 Tue, 21 Jul 2026 12:54:51 GMT 2026-07-21T12:54:51Z https://repositorio.ufu.br/handle/123456789/48888 Title: Análise da presença de radiações ionizantes em materiais diários utilizando câmara de nuvens Abstract: The cloud chamber is a particle detector that enables the direct visualization, with the naked eye, of the tracks left by ionizing radiation. The objective of this work was the optimization of a thermoelectric cloud chamber prototype already described in the literature, aiming to improve its robustness, safety, and thermal efficiency, for its subsequent application in the qualitative analysis of radioactivity in everyday materials. A visual analysis was carried out on several sources, such as a 232Th electrode, an old alarm clock hand containing 226Ra, uranium glass, and 222Rn. The chamber demonstrated a high capacity to distinguish the visual signatures of α particles (short and dense tracks) and β particles (long and erratic tracks), in addition to background radiation. The analysis allowed the visual demonstration of complex physical concepts, such as secular equilibrium and radioactive decay. Its optimization proved effective for teaching and research in the field of nuclear physics Fri, 06 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48888 2026-02-06T00:00:00Z https://repositorio.ufu.br/handle/123456789/48290 Title: O uso de contraste na Tomografia Computadorizada Wed, 17 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48290 2025-12-17T00:00:00Z https://repositorio.ufu.br/handle/123456789/47797 Title: Estudo de séries temporais e suas aplicações em econofísica usando redes neurais artificiais Abstract: This work falls within the field of Econophysics and aimed to create and evaluate the effectiveness of an Artificial Neural Network (ANN) with a Long Short-Term Memory (LSTM) architecture in forecasting time series of stock prices in the Brazilian capital market. For this purpose, historical daily data were collected for the assets Petrobras (PETR4.SA), Vale (VALE3.SA), and Itaú (ITUB4.SA) over the period from 2005 to 2025. The methodology involved the use of advanced computational models to analyze complex financial systems and included, as part of the data preprocessing, normalization using the MinMaxScaler function and structuring the data into 60-day time windows to feed a deep LSTM model. This model consists of four layers and was regularized using the Dropout technique (with a rate of 0.2) to prevent overfitting. The results on the test set showed varied performance: high accuracy was achieved for the banking sector asset (Itaú), good trend forecasting ability for the oil sector asset (Petrobras), and a quantitatively significant failure for the mining sector asset (Vale), evidenced by a notable error and a "cluster"at the end of the training epoch, occurring only for the Vale asset. It is concluded that, although LSTM networks show great potential for modeling financial time series, their effectiveness strongly depends on the particular characteristics and stability of each asset. This reinforces the idea that the financial market is a complex and non-universal system. Mon, 22 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47797 2025-09-22T00:00:00Z https://repositorio.ufu.br/handle/123456789/47770 Title: Métodos biofísicos utilizados para isolamento de toxinas botrópicas com potencial antitumoral Tue, 23 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47770 2025-09-23T00:00:00Z