Undoped and Iodine (I)–doped chrome oxide (Cr2O3)thin films have been prepared by chemical spray pyrolysis technique at substrate temperatures(773K) on glass substrate. Absorbance and transmittance spectra have been recorded as a function of wavelength in the range (340-800 nm) in order to study the optical properties such as reflectance, Energy gap of allowed direct transition, extinction coefficient refractive index, and dielectric constant in real and imagery parts all as a function of wavelength. It was found that all the investigated parameters affect by the doping ratios.

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

The research include a pulsed Nd: YAG Laser with (300µs) pulse duration in the TEM00 mode at (1.06µm) wavelength for energies between (0.5-3) J was employed to drill Brass material which is use in industrial applications. The process of drill was assisted by an electric field. This resulted in an increase in the hole aspect ratio by the value (45%) and decrease in the hole taper by the value (25%) of its value under ordinary drilling conditions using the same input energy.

Existing leachate models over–or underestimates leachate generation by up to three orders of magnitude. Practical experiments show that channeled flow in waste leads to rapid discharge of large leachate volumes and heterogeneous moisture distribution. In order to more accurately predict leachate generation, leachate models must be improved. To predict moisture movement through waste, the two–domain PREFLO, are tested. Experimental waste and leachate flow values are compared with model predictions. When calibrated with experimental parameters, the PREFLO provides estimates of breakthrough time. In the short term, field capacity has to be reduced to 0.12 and effective storage and hydraulic conductivity of the waste must be increased to

In this research, the possibility of using waste wooden materials (reed and sawdust) was studied to produce sustainable and thermal insulation lightweight building units , which has economic and environmental advantages. This study is intended to produce light weight building units with low thermal conductivity, so it can be used as partitions to improve the thermal insulation in buildings. Waste wooden materials were used as a partial replacement of natural sand, in different percentages (10, 20, 30, and 40) % . The mix proportions were (1:2.5) (cement: fine aggregate) with w/c of 0.4. The values of 28 days oven dry density ranged between (2060-1693) kg/m³.The thermal conductivity decreased from (0.745 to 0.2

Focusing on the negative role of default risk on banks, as it is one of the most important risks facing banks, which are difficult to determine accurately, and its reflection on the indicators of profitability of cash flows. The increasing competition between banks led to an increase in the credit facilities granted by banks, and was accompanied by an increase in exposure to the risks of default, which led to an impact on the level of performance of banks in terms of achieving the required return according to the levels of high competition. Therefore, the problem of this study focused on the extent to which the risk indicators of default affect the profitability indicators of the cash flows of the banks research sample in the profit

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi