https://repositorio.ufu.br/handle/123456789/18919 Tue, 06 Oct 2026 16:08:56 GMT 2026-10-06T16:08:56Z https://repositorio.ufu.br/handle/123456789/50535 Title: Modelo de propagação da COVID-19 em ambientes fechados baseado em autômatos celulares Abstract: COVID-19 has highlighted the importance of understanding the spread of contagious diseases in confined environments, where the proximity and movement of people favor viral transmission. This work aims to develop and evaluate an epidemiological model based on a cellular automaton to simulate the spread of COVID-19 in controlled environments, taking the study by Cavalcante et al. (2021) as a reference. The reference model was understood and implemented in the C language, with the addition of a real-time visualization through the raylib library. The cellular automaton was implemented to represent an SI (Susceptible and Infected) epidemiological model based on the Moore neighborhood and on the pseudorandom movement of individuals throughout the monitored environment. The evaluation was carried out in three stages: reproduction of the experiments from the reference paper, variation of parameters, and variation of the environment layout. The results reproduced the behavior reported in the reference, confirming that both an increase in the number of individuals and in the transmission rate intensify the contagion, while reduced populations result in almost nonexistent transmission. Furthermore, keeping the population density constant, it was found that the spatial structure of the environment does not influence the final outcome of the simulation. These results show that the developed model is able to satisfactorily reproduce the transmission dynamics of COVID-19 in confined environments, reinforcing the importance of social distancing as a control measure. Fri, 11 Sep 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50535 2026-09-11T00:00:00Z https://repositorio.ufu.br/handle/123456789/50471 Title: Geração de números pseudoaleatórios em sistemas quânticos caóticos aplicada à criptografia de imagens Fri, 24 Jul 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50471 2026-07-24T00:00:00Z https://repositorio.ufu.br/handle/123456789/50384 Title: Identificação de usuários maliciosos de bitcoin com floresta randômica Abstract: The expansion and consolidation of Bitcoin as a decentralized financial system have introduced complex challenges to public safety and regulatory compliance, primarily due to pseudo-anonymity exploited by malicious actors for illicit activities such as money laundering, darknet market operations, and ransomware extortions. This study aimed to develop and evaluate a supervised machine learning model capable of classifying transactions and addresses on the Bitcoin network for the automated identification of malicious entities. The methodology utilized public records extracted from the Blockchair platform—spanning blocks 600,000 to 605,999, integrated with ground-truth labels from the Bitcoin Address Behavior Dataset (BABD-13). A Random Forest classifier was trained using balanced class weighting, followed by a contamination heuristic and adress majority voting aggregation. Feature importance analysis demonstrated that economic metrics, particularly transaction fees (fee_per_kwu and fee_per_kb) and transaction amounts in fiat currency, were the most decisive indicators of suspicious behavior. At the transaction level, the model achieved 100% accuracy, a macro precision of 96%, and a recall of 84% for illicit activities. By incorporating the address-level majority voting scheme (θ = 0.5), the system eliminated false negatives, achieving a perfect recall of 100% on malicious addresses and an overall accuracy of 99%, confirming its practical viability for digital forensics and compliance audits within blockchain ecosystems. Wed, 16 Sep 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50384 2026-09-16T00:00:00Z https://repositorio.ufu.br/handle/123456789/50299 Title: Análise da maturidade em gestão de projetos e previsão de desempenho operacional em uma startup de tecnologia Fri, 31 Jul 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50299 2026-07-31T00:00:00Z