DFA e análise de agrupamento aplicadas a perfis de porosidade neutrônico em poços de petróleo

Peng was the first to work with the Technical DFA (Detrended Fluctuation Analysis), a tool capable of detecting auto-long-range correlation in time series with non-stationary. In this study, the technique of DFA is used to obtain the Hurst exponent (H) profile of the electric neutron porosity of the 52 oil wells in Namorado Field, located in the Campos Basin -Brazil. The purpose is to know if the Hurst exponent can be used to characterize spatial distribution of wells. Thus, we verify that the wells that have close values of H are spatially close together. In this work we used the method of hierarchical clustering and non-hierarchical clustering method (the k-mean method). Then compare the two methods to see which of the two provides the best result. From this, was the parameter � (index neighborhood) which checks whether a data set generated by the k- average method, or at random, so in fact spatial patterns. High values of � indicate that the data are aggregated, while low values of � indicate that the data are scattered (no spatial correlation). Using the Monte Carlo method showed that combined data show a random distribution of � below the empirical value. So the empirical evidence of H obtained from 52 wells are grouped geographically. By passing the data of standard curves with the results obtained by the k-mean, confirming that it is effective to correlate well in spatial distribution

Slow Oscillations in the Mouse Hippocampus Entrained by Nasal Respiration

Different types of network oscillations occur in different behavioral, cognitive, or vigilance states. The rodent hippocampus expresses prominentoscillations atfrequencies between 4 and 12Hz,which are superimposed by phase-coupledoscillations (30 –100Hz).These patterns entrain multineuronal activity over large distances and have been implicated in sensory information processing and memory formation. Here we report a new type of oscillation at near- frequencies (2– 4 Hz) in the hippocampus of urethane-anesthetized mice. The rhythm is highly coherent with nasal respiration and with rhythmic field potentials in the olfactory bulb: hence, we called it hippocampal respiration-induced oscillations. Despite the similarity in frequency range, several features distinguish this pattern from locally generatedoscillations: hippocampal respiration-induced oscillations have a unique laminar amplitude profile, are resistant to atropine, couple differentlytooscillations, and are abolished when nasal airflow is bypassed bytracheotomy. Hippocampal neurons are entrained by both the respiration-induced rhythm and concurrent oscillations, suggesting a direct interaction between endogenous activity in the hippocampus and nasal respiratory inputs. Our results demonstrate that nasal respiration strongly modulates hippocampal network activity in mice, providing a long-range synchronizing signal between olfactory and hippocampal networks.