Artificial roughness applied to a Solar Air Heater (SAH) absorber plate is a popular technique for increasing its total thermal efficiency (ηt−th). In this paper, the influence of geometrical parameters of V-down ribs attached below the corrugated absorbing plate of a SAH on the ηt−th was examined. The impacts of key roughness parameters, including relative pitch p/e (6–12), relative height e/D (0.019–0.043), angles of attack α (30–75°), and Re (1000–20,000), were examined under real weather conditions. The SAH ηt−th roughened by V-down ribs was predicted using an in-house developed conjugate heat-transfer numerical model. The maximum SAH ηt−th was shown to be 78.8% as predicted under the steady-state condition

This field experiment was conducted at Research Station B, Department of Horticulture and Landscape Engineering, College of Agricultural Engineering Sciences, University of Baghdad, Jadiriyah during the fall season of 2019-2020 to evaluate the effect of cultivation dates and soil fertilization source on the growth, yield and quality of broccoli. A split plot design within the RCBD design with three replicates was applied as the Max F1 hybrid broccoli seedlings were transferred to the field at two dates 25, Sep. 2019 and 15, Oct. 2019, which were symbolized as A and B, respectively, and occupied at the main plot. After two weeks of cultivation, the soil fertilizers were applied three times during the season in 20 days between each applicati

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp