AlO-doped ZnO nanocrystalline thin films from with nano crystallite size in the range (19-15 nm) were fabricated by pulsed laser deposition technique. The reduction of crystallite size by increasing of doping ratio shift the bandgap to IR region the optical band gap decreases in a consistent manner, from 3.21to 2.1 eV by increasing AlO doping ratio from 0 to 7wt% but then returns to grow up to 3.21 eV by a further increase the doping ratio. The bandgap increment obtained for 9% AlO dopant concentration can be clarified in terms of the Burstein–Moss effect whereas the aluminum donor atom increased the carrier's concentration which in turn shifts the Fermi level and widened the bandgap (blue-shift). The engineering of the bandgap by low

AlO-doped ZnO nanocrystalline thin films from with nano crystallite size in the range (19-15 nm) were fabricated by pulsed laser deposition technique. The reduction of crystallite size by increasing of doping ratio shift the bandgap to IR region the optical band gap decreases in a consistent manner, from 3.21to 2.1 eV by increasing AlO doping ratio from 0 to 7wt% but then returns to grow up to 3.21 eV by a further increase the doping ratio. The bandgap increment obtained for 9% AlO dopant concentration can be clarified in terms of the Burstein–Moss effect whereas the aluminum donor atom increased the carrier's concentration which in turn shifts the Fermi level and widened the bandgap (blue-shift). The engineering of the bandgap by

Variation orders are an on-going phenomenon in construction and industry projects worldwide, particularly in the province of Sulaimani, where the project's damage from cost and schedule overrun because of variation orders. However, the effect on project costs and time overrun of variation order has yet to be identified. This study evaluates the impact of variation orders on the cost and time off in the Sulaimani governorate. Two hundred twenty-eight projects from various construction sectors built between 2007-2012 were adopted to calculate the contract cost and schedule overruns due to variation orders. Data analysis was applied in the study were descriptive statistics. One-way ANOVA was also applied to determine w

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM