This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R² of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated.

Obstructive sleep apnea/hypopnea Syndrome (OSAHS) is the most common form of Sleep Disordered Breathing (SDB) and it is estimated to affect approximately 15% of US adult population. In this paper, we report on the results of in vivo experiments of an ultrasonic device for the non-invasive detection of obstructive sleep apnea/hypopnea (OSAH). A description of the ultrasonic system used is presented, followed by the results of a full night sleep study. The findings show a significant difference in the spectral features extracted from the received ultrasonic waveform during apneic breathing, compared to the hyperventilation that follows. Therefore, the findings indicate the feasibility of developing an ultrasonic detection device for low cost diagnosis of SDB.