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The article presents the authors' views on the responses of several scholars to their article "Birnbaum-Saunders Distribution (BSD): A review of models, analysis and applications." These scholars include college professors Augustine Wong, Artur Lemonte and Gauss Cordeiro, and Biswabrata Pradhan. Topics include inferential methods for classical BSD parameters, expectation-maximization algorithms for determining maximum-likelihood estimate, and unified skew-ellipitical BS distribution.

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area. [ABSTRACT FROM AUTHOR]

An interview with Mario A. de Oliveira, associate professor at Mato Grosso Federal Institute of Technology in Brazil, on his research Embedded Application of Convolutional Neural Networks on Raspberry Pi for Structural Health Monitoring, is presented. Topics of the interview include structural health monitoring (SHM), the significance of using convolutional neural networks algorithm in SHM, and his plan for the development of his work.