Ge-Au infrared photoconductive detection was prepared from germanium single crystal which were doped with different gold concentration using thermal evaporation. The spectral resonsivity (Rλ), spectral detectivity (D*) were determined as function of wavelength, also the resistance, conductivity in dark and with illumination to infrared radiation, the gain and relative photo response have been measured with different gold concentration. Remarkable improvements in the photoresponse gain were observed for the highest resistance specimen at the expense of spectral detectivity values.
Begomoviruses infecting zucchini squash were investigated. Leaf samples were collected from zucchini squash growing areas in Baghdad (Jhadryaa and Yusufiyah), Babylon (Jibela and Mahmudiyah) and Diyala (Khan Bani Saad) Provinces. Samples were screened for the presence of begomoviruses using polymerase chain reaction (PCR) and Deng genus specific primers. Sixteen out of 40 samples were begomovirus positive. Sequence analysis confirmed the detection of Tomato leaf curl Palampur virus (TLCPALV)
The main problem established by a discovery of a thyroid nodule is to discriminate between a benign and malignant lesion. Differential diagnosis between follicular thyroid cancer (FTC) and benign follicular thyroid adenoma (FTA) is a great challenge for even an experienced pathologist and requires special effort. A developing number of some encouraging IHC markers for the differential diagnosis of thyroid lesions have emerged, including, Hector Battifora mesothelial (HBME-1) and galectin-3 (Gal-3). There was significant positive correlation between Galectin-3 and HBME-1 in follicular carcinoma and follicular variant of papillary carcinoma (r= 0.380, P= 0.041) and (r= 0.315, P=0.047) respectively. There was no significant correlation between
... Show MoreChange detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac
This study is carried out to investigate the prevalence of Coxiella burnetii (C. burnetii) infections in cattle using an enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) assay targeting IS1111A transposase gene. A total of 130 lactating cows were randomly selected from different areas in Wasit province, Iraq and subjected to blood and milk sampling during the period extended between November 2018 and May 2019. ELISA and PCR tests revealed that 16.15% and 10% of the animals studied were respectively positive. Significant correlations (P<0.05) were detected between the positive results and clinical data. Two positive PCR products were analyzed phylogenetically, named as C. burnetii IQ-No.5 and C. burnet
... Show MoreDetection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
... Show MoreAutism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
... Show MorePersistence of antibiotics in the aquatic environment has raised concerns regarding their potential influence on potable water quality and human health. This study analyzes the presence of antibiotics in potable water from two treatment plants in Baghdad City. The collected samples were separated using a solid-phase extraction method with hydrophilic-lipophilic balance (HLB) cartridge before being analyzed. The detected antibiotics in the raw and finished drinking water were analyzed and assessed using high-performance liquid chromatography (HPLC), with fluorometric detector and UV detector. The results confirmed that different antibiotics including fluoroquinolones and