Innovative instrumentation to strong reduction of the noise levels inside newborn incubators used in the neonatal intensive care units.

In spite of the advent of medical technology, modern newborn incubators (NIs) do not protect them from high noise levels in the neonatal intensive care units (NICUs). Allied to bibliographical research carried out measurements were made inside the dome of a NIs and the results show that the sound pressure levels, or noises, are much more intense than the levels stipulated by the norm NBR IEC 60.601.2.19 of ABNT. These measurements revealed that the NIs air convection system motor is the main source of excess noise. In view of the above, a project was developed with the objective of significantly reducing the noise level inside the dome by modifying the air convection system. Thus, a quantitative study was developed, based on the experimental method, where a ventilation mechanism was designed, constructed and tested, which operated from the network of medical compressed air, present in the NICUs and maternity rooms. Respectively, before and after the modification of the air convection system, the data of relative humidity, air velocity, atmospheric pressure, air temperature and noise [(64.9% ur/33.1% ur); (0.27 m s-1/0.28 m s-1); (1.013.98 hPa/1.013, 60 hPa); (36.5 °C/36.3 °C); (45.9 dBA/30.2 dBA], were collected by electronic meters that registered the conditions of the external and internal environment of the dome of an NI with a passive humidification system. The noise measurements in the environment showed that there was a strong reduction of 15.7 dBA, or 34.2% of internal noise, measured after the modification of the ventilation system, showing a significative performance of the modified NI. Therefore, our results may be a good choose to improve NI acoustics to enable optimal care of the neonate in the neonatal intensive care units.

Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity.

Due to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, [Formula: see text]. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied [Formula: see text] to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that [Formula: see text] (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.