Através do processo de adsorção estudado neste trabalho busca-se a remoção de corantes encontrados frequentemente em efluentes da indústria têxtil, por meio da adsorção de um material adsorvente obtido da casca do fruto da Paineira (Chorisia speciosa A. St.-Hil), uma espécie encontrada em abundância no Mato Grosso do Sul, Brasil. A cápsula do fruto da Paineira foi submetida a ensaios para verificar a sua eficiência no processo de remoção do corante CIBACRON® Azul FN-R, um dos corantes reativos comumente empregados no tingimento de fibras celulósicas. Neste processo foi estudada a influência das variáveis: grau de agitação e temperatura sobre o grau de remoção do corante contido em um banho residual. Os resultados demonstraram que o material obtido por moagem da casca do fruto da Paineira in natura, possui alta capacidade de adsorção. Foi encontrado no estudo que o grau de agitação tem influência significativa sobre o grau de remoção do corante do banho residual, já a temperatura nos níveis estudados não apresentou influência marcante sobre o grau de remoção do corante. Os melhores resultados, em termos de remoção do corante do banho residual, foram obtidos na temperatura de 25°C e para o grau de agitação de 200 rpm. Conclui-se que o material obtido da cápsula da fibra da Paineira é eficiente na remoção do corante CIBACRON® Azul FN-R.

Abstract Precipitation is an important object of study and plays an important role in the dynamics of rainfall distribution in a region. This study investigated the spatial and temporal variation of precipitation in the São Francisco River Basin (SFRB). A historical series of data from 1989 to 2018 was analyzed, and a random function was decomposed into trend and residual components for analysis of precipitation. Interpolation techniques were used to analyze precipitation spatial behavior over time, using high-resolution precipitation maps. Our results showed that the exponential model prevailed in four periods. The findings also showed a high precipitation variability in the SFRB and enabled us to monitor precipitation behavior over the years, as well as in the different sub-regions in SFRB. Finally, important information was obtained, enabling, for instance, the identification of vulnerable areas suffering from lack of rainfall.

One of the main challenges in plant breeding programs is the efficient quantification of the genotype-by-environment interaction (GEI). The presence of significant GEI may create difficulties for breeders in the selection and recommendation of superior genotypes for a wide environmental network. Among the diverse statistical procedures developed for this purpose, we highlight those based on mixed models and factor analysis that are called factor analytic (FA) models. However, some inferential issues are related to the factor analytic model, such as Heywood cases that make the model non-identifiable. Moreover, the representation of the loads and factors in the conventional biplot does not involve any measurement of uncertainty. In this work, we propose dealing with the FA model using the Bayesian framework with direct sampling of factor loadings via spectral decomposition; this guarantees identifiability in the estimation process and eliminates the need for the rotationality of factor loadings or imposition of any ad hoc constraints. We used simulated and real data to illustrate the method’s application in multi-environment trials (MET) and to compare it with traditional FA mixed models on controlled unbalancing. In general, the Bayesian FA model was robust under different simulated unbalanced levels, presenting the superior predictive ability of missing data when compared to competing models, such as those based on FA mixed models. In addition, for some scenarios, the classical FA mixed model failed in estimating the full FA model, illustrating the parametric problems of convergence in these models. Our results suggest that Bayesian factorial models might be successfully used in plant breeding for MET analysis.

The objective of this paper was to investigate the patterns (trend and seasonality) of the levels of rainfall in the municipality of Aracaju, presenting a methodology to make adequate forecasts for short periods of time. It is known that the different Northeast Brazilian regions (NEB) have high temporal variability in rainfall. Specifically, NEB has been suffering from extreme weather, droughts and floods within the same year, and it is no different in the state of Sergipe, especially in its capital, Aracaju. For that, such predictions are used to anticipate the phenomena and organize mitigating actions. In this study, we opted to use the integrated autoregressive model of seasonal moving averages (SARIMA) which contains two parts: one non-seasonal and another seasonal. Accumulated monthly precipitation data in Aracaju, from 1986 to 2018, showed a decreasing trend and a twelve-month seasonality. Adjusting the class of SARIMA models to this historical series proved to be an adequate alternative for forecasting monthly rainfall in this city. Therefore, through the study of this manuscript, the selected model presented adequate estimates of precipitation, thus suggesting that models of the SARIMA class can be used as a tool for forecasting monthly levels of rainfall in the region under study. These estimates can be used to better manage water resources, anticipating society's future needs, such as long periods of drought, droughts and floods.

The analysis of data sets from multienvironmental trials is of fundamental importance in the final stages of plant breeding programs. In this context, the Additive Main Effects and Multiplicative Interaction (AMMI) model has become a popular method for evaluating genotype responses in different environments. In the present work, the AMMI model was applied, under the bayesian approach, to a set of data from a randomized block experiment with 12 mustard genotypes (varieties) in 6 different environments. The objective was to analyze genotypic stability and adaptability through the AMMI-2 biplot representation, highlighting differences in this approach in relation to the classical AMMI analysis. The results showed the great flexibility of the bayesian method to incorporate a random effect for genotypes, as well as inference to the biplot through regions of credibility for genotypic and environmental scores that describe the effect of the interaction between genotypes by environments (GEI). The regions of credibility built for main effects and bilinear parameters allowed to identify more productive genotypes and to visualize homogeneous subgroups of genotypes and environments in relation to the effect of GEI. The more productive genotypes were G8 and G10 and only G2 was considered statistically stable.

Brachiaria brizantha is the most economically important forage grass in Brazil and is propagated through sowing. Producing high-quality seeds has been a constant challenge due to their uneven maturation. The development and application of precise and non-destructive methods for identifying internal damages to seeds, such as the X-ray test, which quickly indicates the quality of the lots, is of fundamental importance for the seed industry. In this work, the quality of Brachiaria brizantha seeds was analyzed based on the morphological characteristics observed in X-ray images that were related to viability using a mixture model under a Bayesian approach, with the following objectives: i) verify the adequacy of the Bayesian modeling used in the data analysis; ii) associate the efficiency of using radiographs as a way to assess the viability of the seeds; and iii) relate the classifications carried out by evaluators with the probability of originating normal or abnormal seedlings. The methodology applied for the analysis proved to be adequate. Further, the Bayesian estimates for parameters related to internal morphology were established with associated levels of uncertainty, which represents an advantage over usual frequentist methods. Based on the model's estimates, seeds evaluated as potentially unviable by three evaluators had practically no probability of germination and did not germinate in the test applied later. Seeds classified as potentially viable had a high probability of developing into normal seedlings, while 73.27% showed this property in the germination test.

Resumo O Nordeste Brasileiro (NEB) apresenta, como característica marcante, a irregularidade em acumulados de precipitações, o que é decorrência de vários fatores, tais como as características ambientais e de sistemas atmosféricos. Análises estatísticas adequadas são necessárias para ter conhecimento dos níveis de retorno da precipitação em uma região e são muito importantes para auxiliar ações para prevenção de desastres naturais causados por chuvas máximas. O objetivo dessa pesquisa consistiu em apresentar os níveis de retorno da precipitação máxima diária (PMD) em todo o estado da Paraíba, o qual está localizado na NEB. O conjunto de dados foi composto por 238 estações pluviométricas, abrangendo o período de 1994 a 2020. Para a modelagem estatística foi ajustada a distribuição Gumbel, por meio do método da máxima verossimilhança. Os resultados indicaram que o Sertão Paraibano apresentou as maiores estimadas da PMD nos meses de janeiro a março, com volumes superiores a 75 mm. A Mata Paraibana apresentou as maiores estimativas da PMD nos meses de abril a junho, com valores superiores a 125 mm no mês de junho em regiões litorâneas. As informações obtidas sobre os níveis de retorno da PMD são relevantes e podem ser de grande auxílio na implementação de políticas públicas, visando a diminuição dos impactos causados pelas chuvas intensas.