In this investigation, water-soluble N-Acetyl Cysteine Capped-Cadmium Telluride QDs (NAC/CdTe nanocrystals), utilizing N-acetyl cysteine as a stabilizer, were prepared to assess their potential in differentiating between DNA extracted from pathogenic bacteria (e.g. Escherichia coli isolated from urine specimen) and intact DNA (extracted from blood of healthy individuals) for biomedical sensing prospective. Following the optical characterization of the synthesized QDs, the XRD analysis illustrated the construction of NAC-CdTe-QDs with a grain size of 7.1 nm. The prepared NAC-CdTe-QDs exhibited higher PL emission features at of 550 nm and UV-Vis absorption peak at 300 nm. Additionally, the energy gap quantified via PL and UV–Vis were 2.2 eV

  The study focused on the results of first paleostress from thrust fault slip data on Tertiary age of Hemrin North Structure, North of Iraq. The stress inversion was performed for fault slip data using an improved right dihedral model, and then followed by rotational optimization (Georient Software). The trend of the principal stress axes (σ1, σ2 and σ3) and the ratio of the principal stress differences (R) show the main paleostress field is NE-SW compression regime. As well as using Lisle graph and Mohr diagram to determine the magnitudes of palestress.  The values paleostress of the study area were σ1=1430 bars, σ2=632 bars and σ3=166 bar. The large magnitudes of the primary stress axes could be attributed to active tecto

Abstract. Fibrewise micro-topological spaces be a useful tool in various branches of mathematics. These mathematical objects are constructed by assigning a micro-topology to each fibre from a fibre bundle. The fibrewise micro-topological space is then formed by taking the direct limit of these individual micro-topological spaces. It can be adapted to analyze various mathematical structures, from algebraic geometry to differential equations. In this study, we delve into the generalizations of fibrewise micro-topological spaces and explore the applications of these abstract structures in different branches of mathematics. This study aims to define the fibrewise micro topological space through the generalizations that we use in this paper, whi

Leuciscidae species are the abundant and widely distributed fish species in Iraq's inland waters. They are complex species, and morphology makes them difficult to identify. Molecular analysis achieved and confirmed the morphological characters. Twenty specimens of Acanthobrama marmid were collected from two localities at Tigris River, in the middle of Iraq; 15 specimens from the Al-Zubaydia sub-district and five specimens from Al-Tharthar Lake. We used the mitochondrial DNA cytochrome b (cytb) gene to sequence the DNA of A. marmid. The following analysis are compared the sequences with those of other fish genera and species found in the Gene Bank. The barcoding result (DNA sequencing) in fishes found in the same family (Leuciscidae) showed

In this study four species from Solanaceae family was conducted. These four species belong to four different genera (Solanum L. ? Physalis L. ?Withania Pauq. ? Lycium L.) The study included morphological characters of sex organs and their pollen grains for these Iraqi wild plants.The results showed that the position of epipetalous stamens , the shape of anther, their dimensions ,and the length of filaments are important taxonomical characters .On the others hand the shape of their ovaries and stigmas are also important characters in distinguish between these four species .Pollen grains are similar in their general shapes and polarities, they have three germinal furrows and germinal apertures, so they are minor in distinguish between these f

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet