Criminal career-related factors among female robbers in the State of Sao Paulo, Brazil, and a presumed 'revolving-door' situation

Introduction: Risk-taking behaviors, family criminality, poverty, and poor parenting have been frequently associated with an earlier onset of criminal activities and a longer criminal career among male convicts. Objective: This study aims to identify factors related to the onset and recurrence of criminal behavior among female robbers in the State of Sao Paulo - Brazil. Method: It was a cross-sectional study carried out inside a feminine penitentiary in Sao Paulo. From June 2006 to June 2010, 175 inmates convicted only for robbery were recruited to be evaluated about family antecedents of criminal conviction, alcohol and drug misuse, impulsiveness, depressive symptoms, and psychosocial features. Results: Having family antecedents of criminal conviction consistently predicted an earlier onset of criminal activities and a longer criminal career among female robbers. Drug use in youth and the severity of drug misuse were significantly related to the initiation and recurrence of criminal behavior, respectively. Discussion: Prisons must systematically screen detainees and provide treatments for those with health problems in general. Children of inmates should obtain help to modify the negative consequences of their parents' incarceration in order to mitigate the negative consequences of pursuing this 'static' factor.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.