The presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn't scale well beyond two-dimensional plots, which happens whenever the first two components don't capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data's evaluation, permitting increased reliability in the selection of superior cultivars and test environments.Key words: genotype × environment interaction, adaptability and stability, additive main effects and multiplicative interaction model, multienvironment trials, cluster analysis, medoids

A análise multivariada é um conjunto de técnicas estatísticas que permite a análise e interpretação de conjuntos de dados de natureza quantitativa com grande número de variáveis de forma simplificada. Dentre o rol de técnicas encontram-se a análise de componentes principais (ACP) e a análise fatorial (AF) que visam reduzir a quantidade de variáveis agrupando-as de acordo com sua variabilidade. Este artigo visa demonstrar a aplicabilidade das técnicas de ACP e AF na interpretação de dados de crime de 26 estados dos EUA.Palavras-chave: Análise multivariada; análise de componentes principais; análise fatorial; crimes.

Predictive modeling is useful to estimate future events based on collected data and discipline-specific knowledge to guide decision making. Machine learning is one of the most frequently used methods to develop predictive models. There is limited literature in which machine learning techniques have been applied to soybeans' behavior during storage, and thus, the goal of this study was to test different model frameworks that estimate dry matter loss of soybeans utilizing data acquired from a dynamic grain respiration system with different levels of moisture content (12, 14, 18, and 22%, wet basisw.b.) and temperature (25, 30, and 35 C). Five different models were trained and tested with a 2,625 point data set, which was partitioned into a training set (90%) and a testing set (10%). In the training step, 10-fold cross-validation was used to choose the best hyperparameters for each of the models. All fitted models were evaluated using standard metrics of Root Mean Square Error (RMSE), coefficient of determination (R 2 ), dispersion of residual values, and a coefficient of performance. The Random Forest model performed best in terms of the distance between predicted and observed values, with three predicting variables. The use of predictive modeling in the recent Agriculture 4.0 scenario is a promising development for risk management and decision making in the context of grain and oilseed storage. Practical ApplicationsSoy is one of the most important crops in the world. The dry matter loss through respiration is a major problem during storage. The results of this study can aid researchers and the soybean industry in managing storage as a risk management and decision-making tool.

Characterized by persistent fatigue, pain, cognitive impairment and sleep difficulties, Chronic Fatigue Syndrome (CFS) has been common in clinical practice. Studies indicate multiple factors contributing to CFS development: poor sleep, dehydration, psychological stress, hormonal dysfunction, nutrient deficiencies, among others. In risk work conditions, like the shift work of mines, CFS significantly increases the chance of fatal accidents. Work environments of mines suggest the presence of factors that increase the risk of developing CFS. Considering the severity/implications of CFS’s symptoms on the social and professional lives as well as on the economy, efforts are targeting its characterization and prevention. This study aims to assess the risk of CFS by studying cross-sectional data on absenteeism of 621 shift workers, measuring 8 anthropometric and 11 biochemical variables as well as age and gender, amounting 21 variables. After imputation, logistic regression was fitted by Stepwise selection, Lasso and Elastic-Net regularization. Results suggest that the models do not discriminate very well due to noise inherent to the dependent variable. However, all models agree on the effects of Sodium and Total Cholesterol on the risk of absenteeism. The Stepwise model also indicates LDL and Triglycerides as significant factors, both Lasso and Elastic-Net show effects for LDL instead. The Elastic-Net model suggests an effect of Potassium, though inconclusive according to the literature.

A análise multivariada é um conjunto de técnicas estatísticas que permite a análise e interpretação de conjuntos de dados de natureza quantitativa com grande número de variáveis de forma simplificada. Dentre o rol de técnicas encontra-se a análise de correlação canônica (ACC) que visa estabelecer estrutura de relação entre dois grupos de variáveis por meio de combinações lineares que maximizem a correlação entre ambos. O objetivo deste artigo foi utilizar a técnica de ACC para analisar a correlação entre três variáveis psicológicas e quatro variáveis de desempenho acadêmico