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This work reports on the fabrication of sensitive and selective gas sensors fabricated using graphene and/or copper oxide (CuO) nanorods. Herein, three batches of sensors were produced based on graphene/SiO2/Si, CuO nanorods, and graphene/SiO2/Si decorated with CuO nanorods. The nanorods were synthesized chemically and deposited on either graphene/SiO2/Si or glass substrates. The produced sensors were tested against H2S and H2 gases. Sensors based on CuO/graphene/SiO2/Si were found to be selective to H2S and sensitive to concentrations as low as 10 ppm. Furthermore, the sensors could detect H2S at room temperature which indicates their low power consumption. The produced sensors have potential for applications in industries that include release of H2S such as petroleum refineries. This work was supported by both Qatar National Research Fund (QNRF) under a grant number UREP19-078-2-037 , and Qatar University fund under a grant number QUCP-CAS- 1718-1 . Scopus

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials , which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their “maximum motion displacement” plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low. publishedVersion