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A inveja é um fenômeno psicológico comum às diferentes culturas. O objetivo foi estimar evidências de validade da Benign and Malicious Envy Scale (BeMaS), instrumento que propõem a mensuração de duas dimensões da inveja: benigna e maliciosa, em adultos brasileiros. Participaram 409 adultos não clínicos de ambos os sexos (M=28.5±10.4; 74.3% mulheres). Foram obtidas evidências de validade baseadas na estrutura interna, na relação com outras variáveis (ansiedade, depressão, distresse psicológico global, e tríade sombria da personalidade). A Análise Fatorial Confirmatória sugeriu adequação da estrutura composta por dois fatores (CFI=.954; TLI=.934; RMSEA=.087). Adicionalmente, obteve-se indicadores de invariância do modelo de medida entre adultos e adolescentes (amostra recuperada de estudo prévio). Por fim, verificou-se relações com variáveis externas coerentes com as hipóteses teóricas. Os resultados sugerem adequação da BeMaS para mensuração da inveja em adultos brasileiros e demonstra potencialidade do instrumento para ser empregado em futuras pesquisas.

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This paper implemented Machine Learning techniques to predict sales success of a large Brazilian food company. The strategy explores the use of recorded data to apply a pattern recognition process based in Multilayer Perceptrons (MLP) algorithms and thus proposes an efficient management of business proposals. As a research methodology, a database with information collected over 4 years (23,093 records) in an Enterprise Resource Planning (ERP) system was evaluated and standardized. Then sales success prediction experiments were performed, using MLPs of the following types: Standard Backpropagation, Backpropagation Momentum, Resilient Propagation, Backpropagation with Weight Decay and Quick Propagation. Resampling by test and validation sets was used, with the proportion variation of 65-35% and 75-25%. The number of neurons in the hidden layer were {3,5,7,10} and the periods of interaction were {50;100;1,000;5,000}. The Receiver Operating Characteristics (ROC curve) and its Area Under the Curve (AUC) were employed to evaluate the performance, considering the normalization of the measured AUCs between 0 and 1. The experiments are focused on the annual and monthly prediction of sales success, according to the profile of the customer. The main achievements of this work were: a methodology for collecting and standardizing the data of the company; an analysis of the best MLP configuration, according to the profile of the customer; and a structure that has an AUC between 0.975 and 0.983, with a processing time between 4.2 and 4.8 seconds, capable of supporting the decision taking of the company and reducing the loss of potential contracts (which reached the order of more than $180,000.00 Brazilian reais). Besides, the implemented approach allows the prediction of renegotiations and success cases.