Economic heavy minerals (EHMS) in the Quaternary Stream Sediments of the Wadi Ibib were characterized mineralogically and chemically via optical microscopy, grain size distribution analysis, heavy liquid separation, X-ray diffraction (XRD), energy-dispersive X-ray fluorescence (EDXRF), and scanning electron microscopy (SEM). Information gathered from characterization studies confirmed that the heavy mineral content (specific gravity of more than 2.89) in the Ibib samples ranged between 8.18 and 17.52% by mass with an average of about 12.56% by mass. EDXRF data analyses also manifested that the content of ilmenite in the Ibib sample reached 0.2%, zircon 0.08%, rutile 0.07%, leucoxene 0.06%, almandine garnet 0.022%, cassiterite 0.007%, xenotime 0.006%, monazite 0.0008%, and magnetite was about 0.29% with high proportion of heavy silicates. With regard to raising the grade of the EHMS, heavy mineral concentrate was obtained through a rougher step on Wilfley Shaking Table No. 13, and then it was followed by two scavenging steps for the highest recovery obtained. The gravimetric concentration steps succeeded in raising the heavy mineral assay from 12.17% to 53.41% with a recovery of 80.42% in a yield of 20.38%. Finally, magnetic separation operations were conducted via low- and high-intensity magnetic separators at different intensities in an attempt to separate and obtain clean concentrates of EHMS that were ready for use in various modern technology industries.

Today, health monitoring systems for turbine engines have become a vital requirement in the aviation industry. In this paper, different fault detection methods of turbine engines are reviewed based on previous research to reveal the importance of the problem and existing challenges. The existing methods use the engine signals for diagnostics, which are heavily affected by operating conditions and disturbances. The faults effect on the performance charts of the F100-PW-220 engine is detected by neural network technique to alleviate the signal variation problem. Some common faults in this type of engine are modeled, including compressor fouling, turbine blade corrosion, and fuel injection problems. The proposed method is effective in a wide range of engine working conditions such as first moments of take-off with afterburner, take-off at 0.1 M, subsonic cruise flight at 0.8 M without afterburner in 10000, 20000, and 40000 feet altitude, supersonic cruise flight at 1.6 M with afterburner in the same altitudes. The cascade neural network with probabilistic transfer functions is used in this paper and shows satisfactory fault detection, while the required training dataset is much less than the previous works. This method facilitates the fast implementation of the system due to the small training dataset and improves the diagnostics accuracy over operational time.

A direct conversion modulator-demodulator with even harmonic mixers with emphasis on noise analysis is presented. The circuits consist of even harmonic mixers (EHMS) realized with antiparallel diode pairs (APDPs). We evaluate the different levels of I/Q imbalances and DC offsets and use signal space concepts to analyze the bit error rate (BER) of the proposed transceiver using M-ary QAM schemes. Moreover, the simultaneous analysis of the signal and noise has been presented.