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Abstract-The development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry.

Agradeço a Deus, por ter me guiado até o caminho do doutorado e por te me dado forças para que eu chegasse até aqui. Agradeço ao Prof. Dr. Evandro Luis Linhari Rodrigues, pela oportunidade de fazer um doutorado, pela orientação, pela conduta no desenvolvimento deste trabalho, pela amizade conquistada nesses anos, pela compreensão e paciência nos bons e maus momentos. Meu sincero e eterno agradecimento. Agradeço a meus pais, pelo esforço feito a vida toda para eu chegasse até aqui, pelo incentivo e força nesses anos. Não sei o que seria de mim sem vocês. Ao meu irmão Alex, pelo incentivo, preocupação e palavras de apoio durante esses anos.

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Establishment of pregnancy after embryo transfer is the ultimate goal of an embryo transfer program and increasing pregnancy rates and reducing pregnancy loss are mandatory. The utilization of treatments to improve conception rates in recipient mares has been the focus of several research groups over the last years and the results are controversial. Some studies using human chorionic gonadotrophin (hCG) found promising results. Our hypothesis was that hCG administration would cause an additional stimulation on luteal function, uterine and luteal vascularization and progesterone concentration, and the mares would have increased uterine and cervix tone. Therefore, in the present study the effects of hCG administration to induce ovulation, on day 0 (day of ovulation) or day 5 postovulation were evaluated on corpus luteum characteristics, reproductive tract vascularization, and serum progesterone concentration from ovulation until day 15 postovulation. Groups were: G1: (control)-no hCG; G2: 2500 IU of hCG to induce ovulation when a follicle greater than 35mm and uterine edema were detected; G3: 2500 IU hCG on day 0; G4: 2500 IU hCG on day 5 postovulation. Twelve mares were randomly assigned to each group, during consecutive cycles, in a Latin Square experimental design, in a total of 48 cycles. Doppler ultrasound evaluations were performed daily from day 0 until day 15 postovulation, including mesometrial vascularity, endometrial vascularity and corpus luteum vascularity. Blood samples were collected for serum progesterone concentration. Data was analyzed using the Proc Glimmix SAS Procedure for nonparametric variables and Proc Mixed for parametric parameters. There was no treatment effect for all variables studied (P > 0.05). Characteristics were only affected by day (P < 0.05). It can be concluded that hCG administration at the time points suggested in the current study did not alter the characteristics evaluated.

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