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Este artigo é um recorte do trabalho de conclusão de curso (TCC) realizado como parte do curso de Licenciatura em Ensino de Matemática com habilitação em Estatística na Universidade Pungue, Extensão de Tete, no ano letivo de 2021. O estudo visa analisar os fatores que influenciam o rendimento acadêmico em Matemática dos alunos da 10ª classe na Escola Secundária Heróis Moçambicanos de Moatize. Utilizou-se uma abordagem quantitativa aplicada, com objetivos explicativos e um levantamento estatístico. Devido à extensa população-alvo, adotou-se uma amostragem por conveniência, resultando em uma amostra de 421 alunos. Os dados foram coletados por meio de um questionário com questões fechadas para embasar a pesquisa. A análise dos dados foi realizada principalmente por meio da técnica de regressão linear múltipla, auxiliada por análises descritivas e de frequências. A classe analisada é composta por seis turmas, divididas em três sub-turmas por turma, totalizando 452 alunos. Os resultados revelaram a construção de um modelo de regressão linear múltipla, indicando que a variável “número de refeições” (NRef) possui o maior impacto no rendimento acadêmico em Matemática. O modelo sugere que os alunos tendem a ter um maior sucesso na disciplina quando fazem, em média, três refeições diárias, conforme sugerido pela distribuição da variável NRef.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi‐step, challenging, and time‐consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open‐source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter‐individual variability (IIV) model, the inter‐occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development. [ABSTRACT FROM AUTHOR]

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design—single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0‐24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter‐individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model‐informed drug development. [ABSTRACT FROM AUTHOR]

This paper presents a two-dimensional (2D) numerical model of soil erosion and sediment transport resulting from rainfall induced overland flow. It is a spatial and temporal dynamic model combining physical and empirical laws and comprises: i) An overland flow module that solves the two-dimensional unsteady water flow equations on an infiltrating surface; ii) A soil infiltration module that uses a combined Horton-SCS scheme; and iii) A soil erosion and sediment transport module that solves the two-dimensional sediment transport equation, distinguishing between rill erosion, interrill erosion and sediment deposition.

Understanding the temporal and spatial dynamic of soil temperature is crucial for agriculture, since it affects soil evaporation and soil biological activity, influencing seed germination, root growth and absorption of water and nutrients by crops, and ultimately the runoff process. Mulching is one of the most applied soil and water conservation method for soil moisture and soil temperature variations control. This study aimed to investigate the influence of three different mulch types (dried elephant grass straw, dried cashew tree leaves and coconut powder or coir dust), applied with three area densities (2, 4 and 8 t ha−1), on the soil surface temperature, under field conditions in the Brazilian semiarid region. Soil temperature monitoring was carried out using thermocouple sensors and an infrared camera in the Brazilian semiarid region. Results show that mulching provided a buffer zone, dampened soil temperature fluctuations over time, mainly for the 8 t ha−1 density. Mulching reduced soil temperature during the hotter period of the day and increased soil temperature during the night period.