Background: The hyperventilation test is used in clinical practice for diagnosis and therapeutic purposes; however, in the absence of a standardized protocol, the procedure varies significantly, predisposing tested subjects to risks such as cerebral hypoxia and ischemia. Near-infrared spectroscopy (NIRS), a noninvasive technique performed for cerebral oximetry monitoring, was used in the present study to identify the minimum decrease in the end-tidal CO2 (ETCO2) during hyperventilation necessary to induce changes on NIRS. Materials and Methods: We recruited 46 volunteers with no preexisting medical conditions. Each subject was asked to breathe at a baseline rate (8–14 breaths/min) for 2 min and then to hyperventilate at a double respiratory rate for the next 4 min. The parameters recorded during the procedure were the regional cerebral oxyhemoglobin and deoxyhemoglobin concentrations via NIRS, ETCO2, and the respiratory rate. Results: During hyperventilation, ETCO2 values dropped (31.4% ± 12.2%) vs. baseline in all subjects. Changes in cerebral oximetry were observed only in those subjects (n = 30) who registered a decrease (%) in ETCO2 of 37.58% ± 10.34%, but not in the subjects (n = 16) for which the decrease in ETCO2 was 20.31% ± 5.6%. According to AUC-ROC analysis, a cutoff value of ETCO2 decrease >26% was found to predict changes in oximetry (AUC-ROC = 0.93, p < 0.0001). Seven subjects reported symptoms, such as dizziness, vertigo, and numbness, throughout the procedure. Conclusions: The rise in the respiratory rate alone cannot effectively predict the occurrence of a cerebral vasoconstrictor response induced by hyperventilation, and synchronous ETCO2 and cerebral oximetry monitoring could be used to validate this clinical test. NIRS seems to be a useful tool in predicting vasoconstriction following hyperventilation.

The present study aimed to estimate the physical properties of the diesel-diesel-biodiesel ternary blends, using artificial neural networks, also known as ANNs. The input data used to estimate the properties was the percentage in which each component was used to obtain the blend. Using two hydrofined diesel fuels from a local refinery and three biodiesel samples synthesized in the university laboratory, a total of 114 blends, both binary and ternary, were obtained. The ANN training database was comprised of exclusively 96 binary blends, from the total of 114. The predictions were made on the remaining 18 ternary blends. All the predictions were within the error mentioned in the standard, concluding the fact that the created ANNs had a rate of 100% accuracy.

The first part of this study analyzed how the blending type of the components used in biodiesel synthesis through transesterification influences yield and the physical properties of the biodiesel. The second part of this study emphasized the way the added proportions of the synthesized biodiesel samples influenced the final diesel-biodiesel blend. This study concluded that the ultrasonic mixing method could replace classical mechanical mixing by being similar both in terms of yield and physical properties.

The purpose of this paper is to compare two different optimization methods, used in acquiring diesel-biodiesel blends. There were used five types of samples in order to enable the optimization of the final blend: there were chosen two types of hydrofined diesel fuel and there were synthesized three original types of biodiesel. The first optimization method used, dual simplex, is a classical method being used in solving linear programming problems. The second optimization method, the genetic algorithms, falls in the type of artificial intelligence algorithms, being an evolutionary method used when the problem requires searching an optimal solution in a great variety of valid solutions.