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The Diplostomidae include a large group of flatworms with complex life cycles and are frequently found parasitizing the eyes and central nervous system of freshwater fishes. The morphological identification of the metacercariae at species level is not always possible. Thus, molecular tools have become essential to assist in the parasite species determination. This study was aimed at describing two diplostomid metacercariae found in freshwater fish in São Paulo, Brazil, based on morphological characters and in the genetic characterization of COI sequences. Our results showed that the two recognized taxa (Tylodelphys sp. and Diplostomidae gen. sp.) appear to be different from the species already described in South America. Tylodelphys sp. differs morphologically from Tylodelphys xenopi, T. mashonense, T. jenynsiae, and T. scheuringi. The metacercariae of T. clavata and T. conifera are smaller than Tylodelphys sp., while T. podicipina is larger than the metacercariae described here. The phylogenetic analysis of COI sequences yielded Tylodelphys sp. as the sister species of Tylodelphys sp. 4, a species reported from the brain of the eleotrid Gobiomorus maculatus in Oaxaca, Mexico. The metacercariae identified as Diplostomidae gen. sp. are morphologically different from the known diplostomid metacercariae and did not match with other diplostomid sequences available. Diplostomidae gen. sp. is recovered as the sister species of Diplostomum ardeae. Although the morphological evidence and the COI sequences differentiate the metacercariae found, the absence of adult specimens of both species precludes the specific designation. This is one of the first papers that use an integrative taxonomy approach to describe the species diversity of diplostomid trematodes in Brazil. [ABSTRACT FROM AUTHOR]

Time series models are often used in hydrology and meteorology studies to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper we introduce a new modeling approach for hydrologic and meteorological time series assuming a continuous distribution for the data, where both the conditional mean and conditional variance parameters are modeled. Bayesian methods using standard MCMC (Markov Chain Monte Carlo Methods) are used to simulate samples for the joint posterior distribution of interest. Two applications to real data sets illustrate the proposed methodology, assuming that the observations come from a normal, a gamma or a beta distribution. A first example is given by a time series of monthly averages of natural streamflows, measured in the year period ranging from 1931 to 2010 in Furnas hydroelectric dam, Brazil. A second example is given with a time series of 313 air humidity data measured in a weather station of Rio Claro, a Brazilian city located in southeastern of Brazil. These applications motivate us to introduce new classes of models to analyze hydrological and meteorological time series. [ABSTRACT FROM AUTHOR]

Abstract: One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variables of the power system itself, but also related to social–economic and climatic factors. Hence, it is imperative for the power suppliers to project and correlate these parameters. This paper presents a study of power load forecast for power suppliers, considering the applicability of wavelets, time series analysis methods and artificial neural networks, for both mid and long term forecasts. Both the periods of forecast are of major importance for power suppliers to define the future power consumption of a given region. The paper also studies the establishment of correlations among the variables using Bayesian networks. The results obtained are much more effective when compared to those projected by the power suppliers based on specialist information. The research discussed here is implemented on a decision support system, contributing to the decision making for acquisition/sale of energy at a future demand; also providing them with new ways for inference and analyses with the correlation model presented here. [Copyright &y& Elsevier]

In 2021, the 88th General Session of the World Assembly of National Delegates to the World Organisation for Animal Health (OIE) recognized the estates of Acre, Paraná, the Rio Grande do Sul, and Rondônia as being free of foot-and-mouth disease (FMD) without vaccination. The certification was also extended to some cities in Amazonas and Mato Grosso. The new national strategic plan for 2026, which focuses on creating and maintaining sustainable conditions to expand FMD-free zones without vaccination, imposes new challenges and requires continuous evaluation of the FMD surveillance system. The objective of this research was to evaluate the FMD surveillance system in Brazil using quantitative models through Bayesian network approaches. The research was conducted using the Continental Surveillance and Information System (SivCont) database for Official Veterinary Services in Brazil, which refers to notified vesicular syndromes. The data on states, reported diseases, source of notification, disease confirmation, and timeliness (TL in days) of the delay by owners in notifying (TL.1) after a suspected case of the disease, and the response of Brazilian Veterinary Services after being notified (TL.2), were analysed. The collected data were analysed using Bayesian networks. It was observed that diseases with symptoms identical to FMD are the most notified events. TL.1 was long (mean of 18.96, CI: 18.33–19.59), and a low number of notifications was observed throughout the study period, which increases the chances of disseminating FMD in the population. Meanwhile, TL.2 suggests appropriate effectiveness of the Veterinary Services responding to suspected cases of FMD with interventions in less than 24 h (mean of 1, CI: 0.68–1.31). This study evaluated the performance of Brazilian Veterinary Services facing the report of vesicular diseases in the period 2004–2018. The results can help the states improve the surveillance system and the transition to the vaccination stop. Furthermore, the analytical method presented in the paper could serve as a model for other countries to evaluate the effectiveness of FMD surveillance systems. [ABSTRACT FROM AUTHOR]

Parametric and non-parametric approaches for rainfall generation have been widely used in hydrological simulation. Both approaches, however, show inherent limitations for representing precipitation extremes. Hence, a stochastic generator able to represent all the range of precipitation amounts is desired. This paper presents a mixed stochastic generator designed for this purpose: the low to moderate rainfall amounts are modeled by the non-parametric approach, avoiding the adjustment of theoretical probability density functions to this range of precipitation and, the extreme rainfall amounts are modeled by the parametric approach, employing the upper-bounded 4-parameter log-normal (LN4) distribution function, with the upper bound estimated under a Bayesian framework, having the at-site probable maximum precipitation (PMP) as a reference value. The mixed generator was used for simulating a 10,000-year long series in the Pará river catchment, in the Brazilian state of Minas Gerais. Results showed that mean daily rainfall, the correspondent standard deviation, coefficient of skewness, number of wet days, and mean monthly and annual rainfall are adequately reproduced. Furthermore, by combining in a logical structure of analysis a prior distribution describing the uncertainties about the PMP estimates and the rainfall data, one can expect more reliable estimates of rare and extreme quantiles and their related uncertainties. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to estimate the preferences of the Central Bank of Brazil after the inflation targeting regime (January 2000 to December 2013), using a DSGE model with microeconomic foundations for a small open economy, based especially on the work of Kam et al. (2009) . The model used in this study considers that the Central Bank minimizes a loss function, taking into account the deviation of inflation from its target, output stabilization, the interest rate smoothing and, unlike the previous works, the exchange rate. The results show that the major concern of the monetary authority in the period was the stabilization of inflation, followed by interest rate smoothing, exchange stabilization and, finally, output stabilization. The large value for the exchange rate smoothing parameter suggests the presence of fear of floating in the Brazilian case. An improved inflation targeting strategy should allow for less Central Bank intervention in the exchange rate market. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to model variations in test-day milk yields of first lactations of Holstein cows by RR using B-spline functions and Bayesian inference in order to fit adequate and parsimonious models for the estimation of genetic parameters. They used 152,145 test day milk yield records from 7317 first lactations of Holstein cows. The model established in this study was additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. Authors modeled the average lactation curve of the population with a fourth-order orthogonal Legendre polynomial. They concluded that a cubic B-spline with seven random regression coefficients for both the additive genetic and permanent environment effects was to be the best according to residual mean square and residual variance estimates. Moreover they urged a lower order model (quadratic B-spline with seven random regression coefficients for both random effects) could be adopted because it yielded practically the same genetic parameter estimates with parsimony. [Copyright &y& Elsevier]

Abstract: This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a longitude–latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil. [Copyright &y& Elsevier]

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the efficiency indices for 60 Brazilian electricity distribution utilities. These scores are obtained by DEA (Data Envelopment Analysis) and Bayesian Stochastic Frontier Analysis models, two techniques that can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental aspect in incentive regulation schemes. In addition, this paper also addresses the problem of identifying outliers and influential observations in deterministic nonparametric DEA models. [ABSTRACT FROM AUTHOR]

Abstract: Toxic drilling fluids and cutting discharges from oil and gas wells adversely affect local environment near the well site. To determine their effects on benthic communities, it is essential to know both the fate of the discharged materials and the chemical concentrations to which biota were exposed during and after drilling. This paper describes a mapping procedure using Bayesian spatial models to define the deposition area of drilling cuttings and fluids from an exploratory well in a deep-sea site in the Campus Basin, RJ, Brazil, taking into account the concentrations of barium and light hydrocarbons used as chemical tracers. The statistical procedures used allow comparisons between sampled sites and the prediction of results at unsampled sites, as well as the delineation of affected areas. The probable impact of the use of non-aqueous fluid (NAF) was measured through observed changes in sea-floor sediments by using Before–After Control-Impact comparisons in a study with three moments: one before drilling and two after drilling. [Copyright &y& Elsevier]