This study describes the development of a semi-physical, real-time nitric oxide (NO) prediction model that is capable of cycle-by-cycle prediction in a light-duty diesel engine. The model utilizes the measured in-cylinder pressure and information obtained from the engine control unit (ECU). From the inputs, the model takes into account the pilot injection burning and mixing, which affects the in-cylinder mixture formation. The representative in-cylinder temperature for NO formation was determined from the mixture composition calculation. The selected temperature and mixture composition was substituted using a simplified form of the NO formation rate equation for the cycle-by-cycle estimation. The reactive area and the duration of NO formation were assumed to be limited by the fuel quantity. The model predictability was verified not only using various steady-state conditions, including the variation of the EGR rate, the boost pressure, the rail pressure, and the injection timing, but also using transient conditions, which represent the worldwide harmonized light vehicles test procedure (WLTC). The WLTC NO prediction results produced less than 3% error with the measured value. In addition, the proposed model maintained its reliability in terms of hardware aging, the changing and artificial perturbations during steady-state and transient engine operations. The model has been shown to require low computational effort because of the cycle-by-cycle, engine-out NO emission prediction and control were performed simultaneously in an embedded system for the automotive application. We expect that the developed NO prediction model can be helpful in emission calibration during the engine design stage or in the real-time controlling of the exhaust NO emission for improving fuel consumption while satisfying NO emission legislation.

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ObjectiveTo determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.MethodsWe performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.ResultsThe best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1–3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4–6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.ConclusionsQEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

In this paper, we proposed a new type high sensitive volatile organic compounds (VOCs) gas sensor array that is based on the pulse width modulation technique. Four different types of solvatochromic dyes and two different types of polymers, were used to make the five different types of sensing membranes. These were deposited on the five side-polished optical fibers by a spin coater to make the five different sensing elements of the array. In order to ascertain the effectiveness of the sensors, five VOC gases were tested. Finally, principal component analysis (PCA) has been used to discriminates different types of VOCs.