Bayesian variable selection in quantile regression with random effects: an application to Municipal Human Development Index.

According to the Atlas of Human Development in Brazil, the income dimension of Municipal Human Development Index (MHDI-I) is an indicator that shows the population's ability in a municipality to ensure a minimum standard of living to provide their basic needs, such as water, food and shelter. In public policy, one of the research objectives is to identify social and economic variables that are associated with this index. Due to the income inequality, evaluate these associations in quantiles, instead of the mean, could be more interest. Thus, in this paper, we develop a Bayesian variable selection in quantile regression models with hierarchical random effects. In particular, we assume a likelihood function based on the Generalized Asymmetric Laplace distribution, and a spike-and-slab prior is used to perform variable selection. The Generalized Asymmetric Laplace distribution is a more general alternative than the Asymmetric Laplace one, which is a common approach used in quantile regression under the Bayesian paradigm. The performance of the proposed method is evaluated via a comprehensive simulation study, and it is applied to the MHDI-I from municipalities located in the state of Rio de Janeiro.

Bayesian factor models for multivariate categorical data obtained from questionnaires.

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology, where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.

Carvacrol and linalool co-loaded in ß-cyclodextrin-grafted chitosan nanoparticles as sustainable biopesticide aiming pest control.

Pesticides are the main tactics for pest control because they reduce the pest population very fast and their efficiency does not depend on abiotic factors. However, the indiscriminate use of these substances can speed up the development of resistant populations and causing environmental contamination. Therefore, alternative methods of pest control are sought, such as the use of botanical compounds. Nanoencapsulation of volatile compounds has been shown to be an important tool that can be used to overcome the lack of stability of these compounds. In this work, we describe the preparation and characterization of chitosan nanoparticles functionalized with ß-cyclodextrin containing carvacrol and linalool. The toxicity and biological activity were evaluated. Decreases of toxicity were observed when the compounds were nanoencapsulated. The nanoparticles presented insecticidal activity against the species Helicoverpa armigera (corn earworm) and Tetranychus urticae (spider mite). In addition, repellent activity and reduction in oviposition were observed for the mites.