Cartographic Enginnering Department

Technological advancement the information has spread with great speed. This fact has contributed to the violence news become more frequent in the media. An important characteristic for the analysis location crime is understood that the event occurs at locations not accidental or unexpected. It's this feature that makes maximum Cartography is a powerful tool for research, it involves location. Thus, the article aims at reporting the experience of understanding aimed at mapping the spatial distribution of the occurrences crimes of violence against property in João Pessoa -Paraíba -Brazil, in the month of May 2012, presenting the phenomenon in thematic maps, which show the points where there are crimes with firearms and knives, as well as the map of high-density areas of crime. Specifically with the conclusion of this article can be identified as the cartography is useful for spatial analysis and may subsidize actions of municipal, state and federal, and local civil society, aimed at combating violence and crime in the city. The results shows that the mapping crime or any other phenomenon is important because it allows to know how it is distributed through space, and thus able to monitor mitigation policies. Therefore, the Cartography occupies an important place among the sciences to help fight crime, since based on a map with the spatial distribution of crimes in an area can provide efficient operational safety features. By analyzing the maps it is clear that the amount of crimes using firearms was very big, so it would be interesting to disarmament campaigns in neighborhoods, followed by installation of core security on site, minimizing the increase in cases of crimes violence against property. Cartography and crime mapping analysis can assist in planning, and implementation of action plans to minimize the cost of operations and more effectively, since the study area is already known and the whole strategy can be applied.

This paper presents three modeling techniques to predict return of consciousness (ROC) after general anesthesia, considering the effect concentration of the anesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anesthetic and the analgesic mean dose during surgery, and the age of the patient, proved to have significantly high correlation coefficients. Variables like the mean bispectral index value during surgery, duration of surgery did not present a statistical relation with ROC. Stochastic regression models were built using the variables with higher correlation. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Thirdly, radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anesthetic drug effect concentration at awakening. Clinical data was used to train and test the models. The stochastic models and the fuzzy models proved to have good prediction properties. The RBF network models were more biased towards the training set. The best balanced performance was achieved with the fuzzy models.