In this research, Mn-doped TiO2 thin films were grown on glass, Si and OIT/glass substrates by R.F magnetron sputtering technique with thicknesses (250 nm) using TiO2:Mn target under Ar gas pressure and power of 100 Watt. Through the results of X-ray diffraction, the prepared thin films are of the polycrystallization type after the process of annealing at 600°C for two hour The average crystalline size were 145.32, 280.97 and 261.23 nm for (TiO2:Mn) thin film on glass, Si and OIT/glass substrates respectively, while the measured surface roughness is between 0.981nm and 1.14 nm. The fabricated (TiO2:Mn) thin film on glass sensors have high sensitivity for hydrogen( H2 reducing gas) compared to the sensitivity for hydrogen gas on Si and OIT/

Image processing applications are currently spreading rapidly in industrial agriculture. The process of sorting agricultural fruits according to their color comes first among many studies conducted in industrial agriculture. Therefore, it is necessary to conduct a study by developing an agricultural crop separator with a low economic cost, however automatically works to increase the effectiveness and efficiency in sorting agricultural crops. In this study, colored pepper fruits were sorted using a Pixy2 camera on the basis of algorithm image analysis, and by using a TCS3200 color sensor on the basis of analyzing the outer surface of the pepper fruits, thus This separation process is done by specifying the pepper according to the color of it

In this research tri metal oxides were fabricated by simple chemical spray pyrolysis technique from (Sn(NO₃)₂.20 H₂O, Zn(NO₃)₂.6 H₂O, Cd(NO₃)₂.4 H₂O) salts at concentration 0.1M with mixing weight ratio 50:50 were fabricated on silicon substrate n-type (111). (with & without the presence of grooves by the following diemensions (20μm width, 7.5μm depth) with thickness was about ( 0.1 ±0.05 µm) using water soluble as precursors at a substrate temperature 550 ºC±5, with spray distance (15 cm) and their gas sensing properties toward H₂S gas at different concentrations (10,50,100,500 ppmv) in air were investigated at room te

Surface Plasmon Resonance (SPR)-based plastic optical fiber sensor for estimating the concentration and refractive index of sugar in human blood serum. The sensor is fabricated by a small part (10mm) of optical fiber in the middle is embedded in a resin block and then the polishing process is done, after that it is deposited with about (40nm) thickness of gold metal. The blood serum is placed on gold coated core of an Optical grade plastic optical fiber of 980 µm core diameter.

Limitations of the conventional diagnostic techniques urged researchers to seek novel methods to predict, diagnose, and monitor periodontal disease. Use of the biomarkers available in oral fluids could be a revolutionary surrogate for the manual probing/diagnostic radiograph. Several salivary biomarkers have the potential to accurately discriminate periodontal health and disease. This study aimed to determine the diagnostic sensitivity and specificity of salivary interleukin (IL)‐17, receptor activator of nuclear factor‐κB ligand (RANKL), osteoprotegerin (OPG), RANKL/OPG for differentiating (1) periodontal health from disease and (2) stable a

Artificial neural networks usage, as a developed technique, increased in many fields such as Auditing business. Contemporary auditor should cope with the challenges of the technology evolution in the business environment by using computerized techniques such as Artificial neural networks, This research is the first work made in the field of modern techniques of the artificial neural networks in the field of auditing; it is made by using thesample of neural networks as a sample of the artificial multi-layer Back Propagation neural networks in the field of detecting fundamental mistakes of the financial statements when making auditing. The research objectives at offering a methodology for the application of theartificial neural networks wi

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope