Avaliação e monitoramento de políticas públicas: criação de um modelo sistêmico aplicado ao Instituto de Assistência Técnica e Extensão Rural do Rio Grande do Norte (EMATER-RN)

The nature of this thesis is interventionist and aims to create an alternative on how to control and evaluate the public policies implementation developed at the Institute for Technical Assistance and Rural Extension of Rio Grande do Norte State. The cenarium takes place in a public institution , classified as a municipality that belongs to the Rio Grande do Norte government and adopts the design science research methodology , where it generates a set of artifacts that guide the development of a computerized information system . To ensure the decisions, the literature was reviewed aiming to bring and highlight concepts that will be used as base to build the intervention. The use of an effective methodology called Iconix systems analysis , provides a software development process in a short time . As a result of many artifacts created by the methodology there is a software computer able of running on the Internet environment with G2C behavior, it is suggested as a management tool for monitoring artifacts generated by the various methods. Moreover, it reveals barriers faced in the public companies environment such as lack of infrastructure , the strength of the workforce and the executives behavior

Análise de agrupamentos dos dados de DFA oriundos de perfis elétricos de indução de poços de petróleo

The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis