Visible-light photodetectors constructed Fe2O3 were manufactured effectively concluded chemical precipitation technique, films deposited on glass substrate and Si wafer below diverse dopant (0,2,4,6)% of Cl, enhancement in intensity with X-ray diffraction analysis was showed through favored orientation along the (110) plane, the optical measurement presented direct allowed with reduced band gap energies thru variation doping ratio , current–voltage characteristics Fe2O3 /p-Si heterojunction revealed respectable correcting performance in dark, amplified by way of intensity of incident light, moreover good photodetector properties with enhancement in responsivity occurred at wavelength between 400 nm and 470 nm.

A thin film of SnSe were deposited by thermal evaporation technique on 400 ±20 nm thick glass substrates of these films were annealed at different temperatures (100,150,200 ⁰C), The effect of annealing on the characteristics of the nano crystalline SnSe thin films was investigated using XRD, UV-VIS absorption spectroscopy, Atomic Force Microscope (AFM), and Hall effect measurements. The results of X-ray displayed that all the thin films have polycrystalline and orthorhombic structure in nature, while UV-VIS study showed that the SnSe has direct band gap of nano crystalline and it is changed from 60.12 to 94.70 nm with increasing annealing temperature. Hall effect measurements showed that all the films have a positive Hall coeffic

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

This work aimed PVA nanofibers in a range of concentrations were successfully manufactured via electrospinning. PVA NFs/Si was effectively prepared using the electrospinning process. The structural, morphological, optical and electrical properties of the prepared PVA were studied using XRD, FE-SEM, UV-Vis spectrophotometer and I-V characteristics, respectively. The amorphous structure of PVA nanofibers was observed. The optical energy gap from ultraviolet to visible was between (2.75 and 2.41) eV, making this compound highly sensitive to visible orange light at 610 nm, with a photosensitivity of 66%. The optical energy gap of PVA/Si heterojunction was utilized to modify this film from the UV to the visible spectrum. As show in the results,

Concentrations of radon were measured in this study for twenty-four samples of soil distributed in six locations on the north part of Iraq. The radon concentrations in soil samples measured by using alpha-emitters registration that emits from Radon (222Rn) in (CR-39) track detector. The concentrations values were calculated by a comparison with standard samples. The results shows that the radon gas concentrations in Darbandikhan City varies from (16.60-34.04 Bq/m3), Halabja City (16.51-23.32 Bq/m3), Al Sulaimaniya City (17.61-32.25 Bq/m3), Koisnjaq City (22.04-35.65 Bq/m3), Shaqlaua City (21.10-29.10 Bq/m3) and Erbil City (22.30-34.63 Bq/m3). The average radon gas concentration in Al Sulaimaniya and Erbil governorate are (22.30 Bq/m3)