https://repositorio.ufu.br/handle/123456789/17752 Tue, 26 May 2026 08:21:58 GMT 2026-05-26T08:21:58Z https://repositorio.ufu.br/handle/123456789/48722 Title: Avaliação das variáveis climáticas: precipitação, temperatura e umidade relativa do ar da cidade de Franca-SP por meio de análise de séries temporais Abstract: Understanding how climatic conditions interfere with agriculture is fundamental, especially for coffee cultivation, one of the main economic activities in the city of Franca-SP, recognized for its relevance in coffee production in Brazil. In this context, the objective of this study was to adjust time series models capable of predicting climatic variables such as monthly precipitation, average temperature, and relative air humidity, contributing to assisting in the decision-making of coffee growers and rural producers. To this end, SARIMA-type models were adjusted using profitable monetary data between January 2011 and May 2025, planned for the period from January to May 2025. The model selection criteria were based on the lowest values of the Akaike Information Criterion (AIC) and the Bayesian Schwarz Criterion (BIC). Predictive per formance was evaluated using the metrics RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MASE (Mean Absolute Scaled Error), and ME (Mean Error). The results in dicated distinct performances for each climatic variation. The total monthly exception showed reasonable performance with the SARIMA (0,0,1)(0,1,1)12 model, possibly due to the high variability characteristic of this variable. The average monthly temperature showed the best performance among the variables tested with the SARIMA (1,0,1)(0,1,1)12 model, demonstra ting high precision in the isolated variables. Relative air humidity showed superior results with the SARIMA (0,0,1)(0,1,1)12 model, although with superior performance compared to tempe rature. Overall, the results indicate that models from the SARIMA family can be useful tools for forecasting climate variations, potentially assisting in planning and decision-making related to coffee crop management. Mon, 16 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48722 2026-03-16T00:00:00Z https://repositorio.ufu.br/handle/123456789/47944 Title: Análise do comportamento das métricas de salto ao longo de uma temporada em um time de voleibol profissional feminino Fri, 26 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47944 2025-09-26T00:00:00Z https://repositorio.ufu.br/handle/123456789/47878 Title: Inferência bayesiana sobre parâmetros genéticos de bovinos da raça Girolando Abstract: This study aimed to estimate genetic parameters associated with milk production in Girolando cattle, using a Bayesian model with the Hamiltonian Monte Carlo (HMC) sampler, implemented through the rstan package in the R software. The dataset used comprises 295 unique animals, totaling 485 observations. The results showed that the estimated heritability was 0.235, with a HPD interval (Highest Posterior Density) from 0.09 to 0.38, suggesting that approximately 23.5% of the variability in milk production is explained by genetic factors, although environmental and management factors also play an important role. The analysis of fixed effects indicated that the blood compositions 5/8H, 3/4H, and 1/2H had the greatest impactson production, suggesting that these genetic compositions have greater potential for improving milk yield. The fractions above indicate the Holstein (H) blood percentage in crosses with the Gir breed. The objectives of the study were successfully achieved, as it was possible to estimate the heritability of milk production and identify the blood compositions with the greatest productive impact, providing support for genetic selection strategies in the herd. As a future perspective, it is recommended to include other fixed-effect variables and more detailed pedigree information to improve the analysis of productive performance, considering both genetic and non-genetic effects. Fri, 09 May 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47878 2025-05-09T00:00:00Z https://repositorio.ufu.br/handle/123456789/47877 Title: Previsão de vendas em marketplace com modelos multi-outputs: estudo de caso olist Mon, 29 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47877 2025-09-29T00:00:00Z