Trachoma epidemiological school survey in the city of Embu das Artes - SP

Purpose: To estimate the trachoma prevalence in school children in Embu das Artes - SP, aiming the implementation of the disease epidemiological surveillance. Methods: The city of Embu das Artes - SP, is 25 km far from the capital of the State. In the years of 2003-2004, a trachoma survey was conducted in a cluster sample of school children with the same methodology of the national trachoma student's survey of the Ministry of Health. Previously to the trachoma active search, activities of health education were performed in all schools. External ocular examinations were done in all students to detect trachoma according to the WHO criteria. All cases of trachoma were notified and their families were submitted to an external ocular examination. The cases were treated with 1% tetracycline ointment or systemic azithromycin. Results: 2,374 students from nine sampled selected public schools were examined. The prevalence of follicular inflammatory trachoma (TF) was 3.1% (IC 95%: 2.4-3.9), varying from 0.5% to 4.2% in the examined schools. The prevalence for males was 3.2% and for females was 3.0%. The greater prevalence (8.6%) was found in 6 year-old children. Conclusion: The disease showed a mild behavior in this city, because no cases of intense inflammatory trachoma or cicatricial trachoma were detected. However, the prevalence was greater than the one found in the city of Sao Paulo. Epidemiological surveillance activities of trachoma must be continuous, mainly in places where the greater prevalence had been found.

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.