The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

We apply our results on random sample size ANOVA to a Brazilian database on cancer registries. The samples sizes will be considered as realizations of random variables. The interest of this approach lies in avoiding false rejections obtained when using the classical fixed size F-tests.

The aim of this paper is to extend the theory of F-tests with random sample sizes to situations when missing observations may occur. We consider the one-way ANOVA with fixed effects. This approach is illustrated through an application to patients affected by melanoma skin cancer, from three different states of Brazil.

ANOVA is routinely used in many situations, namely in medical research, where the sample sizes may not be previously known. This leads us to consider the samples sizes as realizations of random variables. The aim of this paper is to extend one-way random effects ANOVA to those situations and apply our results to a Brazilian database on cancer registries.