DSpace Collection:https://repositorio.ufu.br/handle/123456789/187922026-07-21T12:57:35Z2026-07-21T12:57:35ZAnálise da presença de radiações ionizantes em materiais diários utilizando câmara de nuvenshttps://repositorio.ufu.br/handle/123456789/488882026-07-18T06:18:13Z2026-02-06T00:00:00ZTitle: 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
physics2026-02-06T00:00:00ZO uso de contraste na Tomografia Computadorizadahttps://repositorio.ufu.br/handle/123456789/482902026-02-14T06:21:25Z2025-12-17T00:00:00ZTitle: O uso de contraste na Tomografia Computadorizada2025-12-17T00:00:00ZEstudo de séries temporais e suas aplicações em econofísica usando redes neurais artificiaishttps://repositorio.ufu.br/handle/123456789/477972025-12-06T06:26:22Z2025-09-22T00:00:00ZTitle: 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.2025-09-22T00:00:00ZMétodos biofísicos utilizados para isolamento de toxinas botrópicas com potencial antitumoralhttps://repositorio.ufu.br/handle/123456789/477702025-12-02T06:21:44Z2025-09-23T00:00:00ZTitle: Métodos biofísicos utilizados para isolamento de toxinas botrópicas com potencial antitumoral2025-09-23T00:00:00Z