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Citriculture is one of the most important agricultural activities globally, with Brazil being one of the leading world producers. Thus, such activity is essential for the country's economy and the producers who depend on it. In this sense, the fight against Huanglongbing, one of the most devastating citrus diseases caused by vector insects, is essential to guarantee the quality of the fruit and avoid economic losses. The present work analyzed the counting of insect vectors in a commercial orange orchard in an observational study carried out in the municipality of Paranavaí, state of Paraná, Brazil, using the methodologies of generalized linear mixed models (GLMM) and generalized additive models for location, scale, and form (GAMLSS), with Negative Binomial probability distribution. Data were obtained by counting insects trapped in sticky traps at twelve fixed points in the orchard at three different heights and collected over seven fortnights. The results indicated that the GAMLSS model presented better results by including the linear predictor for modeling the scale parameter associated with the study factors based on the AIC criterion and diagnostic analysis tools.

Isoflavones are composed of phytoestrogens (genistein and daidzein), which can be metabolized by cats. These compounds can promote the maintenance of lean body mass and control food intake. These effects are desirable in neutered animals, as they are predisposed to obesity. The objective of this study was to evaluate the effects of dietary supplementation of 1.0% isoflavone on the metabolizable energy intake, serum concentrations of satiety-related hormones and peptides, and body composition of neutered cats. Sixteen neutered adult cats were blocked by gender and divided into two groups (n = 8): the control group (CG) received a commercial diet, while the isoflavone group (IG) received the same diet supplemented by 1% of isoflavone for 99 days. Computed tomography was performed on the first and last experimental days to assess the animals’ body composition. Satiety challenges were conducted on days 19 and 44. In the last day of the study, blood samples were collected to determine the concentration of insulin, ghrelin, leptin, peptide YY, and GLP-1. A statistical analysis was conducted using R software 3.5.2, considering both the interaction and individual effects of group and time (p < 0.05). The average intake of genistein in the IG was 0.75 ± 0.10 mg/kg body weight, and daidzein intake was 51.73 ± 7.05 mg/kg. No significant individual or interaction effects were observed for any of the analyzed variables. Therefore, the inclusion of 1.0% isoflavone in the diet did not affect the energy requirements, satiety responses, or body composition of neutered adult cats.

Fugitive emissions are unintentionally produced by pipeline leakage and evaporation in industrial processes and contribute 5% of Global Greenhouse Gas emissions (GHG). Frictional wear and thermal fatigue in vehicle exhaust pipe couplings and joints can cause leaks that are not visible and difficult to quantify. It is therefore essential to trace and document these sources. In this work, an experimental survey was conducted on buses in accordance with Regulation (EC) N° 715/2007 of the European Parliament. Statistical methods by means of a priori analysis aided by G∗Power 3.1 software was used to define the required sample. Three random sample groups were stratified and fugitive gases were encased and piped into a bronze tube 5 mm in diameter and 500 mm in length. A Horiba PG-300 analyzer was used to analyze the samples using chemiluminescence and infrared methods. The results proved the existence of fugitive emissions in all samples analyzed with variations of (3.000–27.500 ppm) among the samples for CO2, (6.0–138.5 ppm) and (2.0–5.0 ppm) for CO and NOx, respectively. Statistical analysis showed that engine mileage had no significant influence on NOx emissions, while CO and CO2 emissions increased with mileage. Analysis using Response Surface Methodology (RSM) indicated a trend of increasing concentrations of CO2 and CO for both explanatory variables, mileage and usage time.

We investigated regression models with mixed effects using generalized linear statistics to evaluate germination data from Stevia rebaudiana (Bert) Bertoni. Estimates and validation of statistical parameters were conducted using the “gamlss” package in the R software. Generalized linear mixed effects followed the binomial, the beta-binomial and the multinomial distribution with the logit link to explain data based on the following explanatory variables: seed germinator, plastic tray position on every tier of shelves, illuminance conditions (light and darkness) and seed lots. We did not find differences in proportional responses from seed germinators, but we did find differences in the illuminance conditions, plastic tray position on the tiers of shelves in the seed germinators and seed lots. The estimates of the generalized Akaike information criterion (GAIC), Akaike information criterion (AIC), global deviance (GD) and Bayesian information criterion of Schwarz (BIC) indicate similar goodness-of-fit for the binomial and beta-binomial models. All of the models indicate that the position of the germination tray on every tier of shelves and illuminance conditions affected the proportions of normal seedlings. The seed germination in the plastic tray on the uppermost position under fluorescent day light lamps had an effect on the proportion of normal seedlings of Stevia.

Concerning the specificities of a longitudinal study, the trajectories of a subject's mean responses not always present a linear behavior, which calls for tools that take into account the non-linearity of individual trajectories and that describe them towards associating possible random effects with each individual. Generalized additive mixed models (GAMMs) have come to solve this problem, since, in this class of models, it is possible to assign specific random effects to individuals, in addition to rewriting the linear term by summing unknown smooth functions, not parametrically specified, then using the P-splines smoothing technique. Thus, this article aims to introduce this methodology applied to a dataset referring to an experiment involving 57 Swiss mice infected by Trypanosoma cruzi, which had their weights monitored for 12 weeks. The analyses showed significant differences in the weight trajectory of the individuals by treatment group; besides, the assumptions required to validate the model were met. Therefore, it is possible to conclude that this methodology is satisfactory in modeling data of longitudinal sort, because, with this approach, in addition to the possibility of including fixed and random effects, these models allow adding complex correlation structures to residuals.

In the current work, the adsorption of bisphenol A (BPA) was investigated using a metal–organic framework (MIL-100). MIL-100 was prepared using a homemade microwave oven and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, and physisorption analysis based on the Brunauer–Emmett–Teller BET surface area. Response surface methodology (RSM) was used to optimize the adsorption conditions, i.e., pH and the initial concentrations of BPA and the adsorbent; the BPA adsorption was considered the response. Using a central composite design (CCD), we obtained an experimental model validated by ANOVA and the interaction of two significant factors using RSM to the response. The laboratory experiments using MIL-100 revealed excellent adsorption activity toward BPA in water. The optimum conditions for BPA removal were 1.0 mg MIL-100 concentration and 200 mg/L initial BPA concentration. Under these conditions, the maximum adsorption of 1400 mg/g, which was higher than that previously reported, was obtained due to the favorable physicochemical properties of the adsorbent, such as large surface area, high porosity, and high stability. These results showed that MIL-100 can be an effective adsorbent for water decontamination by BPA removal; furthermore, statistical analysis can be used for adsorption tests, thereby reducing the number of experiments and making the analysis environmentally friendly.

Citrus canker, caused by the bacterium Xanthomonas citri subsp. citri, is one of the most important diseases of citrus. The use of resistant genotypes plays an important role in the management and control of the disease and is the most environmentally sustainable approach to disease control. Citrus canker incidence was recorded in an experiment on nine genotypes of the sweet orange variety 'Pera grafted on four rootstocks. The experiment was started in 2010 and the incidence of citrus canker on the leaves was recorded on a quarterly basis. The incidence data from the experiment were analyzed using a zero-inflated Beta regression model (RBIZ), which is the appropriate method to describe data with large numbers of zeros. Based on the residual analysis, the data fit the model well. The discrete component of the explanatory variable, rootstock, was not significant as a factor affecting the onset of disease, in contrast with the continuous component, genotype, which was significant in explaining the incidence of citrus canker.

Zero-inflated data from field experiments can be problematic, as these data require the use of specific statistical models during the analysis process. This study utilized the zero-inflated negative binomial (ZINB) model with the log- and logistic-link functions to describe the incidence of plants with Huanglongbing (HLB, caused by Candidatus liberibacter spp.) in commercial citrus orchards in the Northwestern Parana State, Brazil. Each orchard was evaluated at different times. The ZINB model with random effects in both link functions provided the best fit, as the inclusion of these effects accounted for variations between orchards and the numbers of diseased plants. The results of this model show that older plants exhibit a lower probability of acquiring HLB. The application of insecticides on a calendar basis or during new foliage flushes resulted in a three times larger probability of developing HLB compared with applying insecticides only when the vector was detected.

The experiment had the objective of fitting regression models to data of the height of the bedding plants cultivated in three multicellular Styrofoam trays with three different cell volumes. We proposed two types of models in the current experiment. First, we fit a model with normal errors and next a model with a skew-normal distribution of errors. The skew-normal regression was suitable for modelling both cases. First, when the model included the time covariate and next when the cell size covariate was part of the model. However, the value of the parameter λ for the multivariate model was very high, which is an indication that the skew-normal model is also not the best. Thus, we suggest further fitting using the skew regression model of t-Student.

The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma ([10.sup.3], [10.sup.3]) in the model [M.sub.1], normal (0, [10.sup.6]) in the model [M.sub.2], uniform (0, [L.sub.sup]) in the model [M.sub.3], exp ([mu]) in the model [M.sub.4] and [L.sub.normal] (p, [10.sup.6]) in the model [M.sub.5] . However, to achieve the convergence in the models [M.sub.4] and [M.sub.5], we applied the u from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the [M.sub.1] and [M.sub.3]. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison.