University of GarmianPasser Journal of Basic and Applied Sciences270659442220200901Antimicrobial Activity of Silver Nanoparticles with Antibiotics Against Clinically Isolated Acinetobacter baumannii515610837010.24271/psr.11ENPyman M.MohamedsalihDepartment of Medical Laboratory Sciences, Charmo University, 46023 Chamchamal, Sulaimani, Kurdistan Region, IraqDana KhdrSabirDepartment of Medical Laboratory Sciences, Charmo University, 46023 Chamchamal, Sulaimani, Kurdistan Region, Iraq0000-0001-6197-7737Journal Article20200409<strong>Antibiotic-resistant bacteria are among the major healthcare problem worldwide and <em>Acinetobacter baumannii </em>is a leading threat among them. In this study, the combined effects of different antibiotics with silver nanoparticles (AgNPs) were assessed against the growth of a clinical isolate of <em>A. baumannii</em>. The bacterial strain was isolated from a hospitalized burned patient in Sulaimanyah- Iraq. Identification of the isolated bacterium was done based on the partial sequence of the 16S rRNA gene and phylogenetic analysis. The growth of the bacterium was totally inhibited by AgNPs at the concentration of (0.2 mg/ml). AgNPs treatment showed a partial synergistic effect with azithromycin (Fractional Inhibitory Concentration Index (FICI) = 0.6) and an additive effect with kanamycin (FICI = 1.67). Not a significant difference in the antimicrobial activities of either ampicillin or tetracycline was observed when they used alone or in combination with AgNPs. Overall, this study may provide a promising future use of azithromycin with AgNPs to treat <em>A. baumannii</em> superficial infections; however, a combination of kanamycin with AgNPs together should be avoided.</strong>University of GarmianPasser Journal of Basic and Applied Sciences270659442220200901Beta–Binomial and Overdispersion with Exchange of the Sample Size over the Probability Interval [0, 1] with Applications636811821410.24271/psr.13ENHanaw AhmedAminDepartment of Mathematics, College of Science, University of Sulaimani, Sulaimani, Kurdistan Region, Iraq0000-0002-4072-0294Rando RasulQadirDepartment of Mathematics, College of Basic Education, University Of Sulaimani, P.O. Box: 46, Sulaimani, Kurdistan Region, IraqJournal Article20200820The beta-binomial model that is generated by a simple mixture model has been commonly applied in the health, physical, and social sciences. In clinical and public health, overdispersion occurs due to biological variation between the subjects of interest. Both the binomial and beta-binomial models are applied to different problems occurring in rational test theory. In this study, we focused on modeling overdispersion for binomial distribution. The main aim was to show a complete and extensive understanding of the beta-binomial model and updated form by broaden its practical applications in the field of breast cancer with hormone medication. It is observed in different independent Bernoulli trials yes/no (<em>x<sub>i</sub></em>=1, 0) experiments with success probabilities 0<<em>p <sub>i</sub></em>< 1 and compare the model in a sequence of <em>n<sub>i</sub></em>. The performance of the maximum likelihood estimates technique that is used in moderate and small samples <em>n<sub>i</sub></em> by a Newton-Raphson iterative method using Matlab package. We have found that using hormones for other treatments have complication leading to breast cancer. We took 20 investigational testers in Hiwa Hospital for cancer treatment in Sulaymaniyah province, with proportion <em>p <sub>i</sub></em> is varying from 9.7% to 50 %. In addition, we concluded that the beta-binomial theory is a good alternative of binomial model. This is due to the fact that the beta-binomial model has provided a robust estimate for events from heterogeneous binomial studies. University of GarmianPasser Journal of Basic and Applied Sciences270659442220201026The Influence of Various Reactants in the Growth Solution on the Morphological and Structural Properties of ZnO Nanorods697511851010.24271/psr.14ENAhmed FattahAbdulrahmanFaculty of Science, University of Garmian, Kalar, Iraqhttps://orcid.org/00Journal Article20200827In the current work, the effect of three different Zinc (Zn) salts as reactants precursors in the growth solution on the characteristic properties of the Zinc oxide (ZnO) nanorods (NRs) was investigated and reported. High quality hexagonal ZnO NRs have been grown on the glass-slide substrates via the chemical-bath deposition (CBD) approach at 90 ºC. The radio-frequency sputtering (RF) technique has been used to coat the 150 nm of ZnO nano-seed layer over the whole glass-slide substrates. The Field-emission scanning electron microscopy (FESEM), the Energy-dispersive characterization (EDX), and the X-ray diffraction (XRD) characterizations have been used to characterize and examination of the morphological, chemical compositional, and structural characteristics with ZnO hexagonal-wurtzite structure of the NRs. The used zinc salts were Zinc-nitrate Hexahydrate (ZNH), Zinc-acetate (ZA), and Zinc-chloride (ZC). The FESEM and XRD results indicated that the change in types of Zinc salts with Methenamine as reactants precursors in the growth (deposition) solution have a remarkable and significant impact on the surface topography (morphology) characteristics and structural characteristics of synthesized ZnO NRs. The average size and average length of the grown ZnO NRs were in the range of (91-529) nm and (1008-3189) nm, respectively. The high aspect ratio was obtained of ZnO NRs synthesized from Zinc-nitrate Hexahydrate salt and was about 11. The highest growth rate was investigated ZnO NRs synthesized from Zinc-chloride salt and was about 17.716 nm/min. The average crystalline size of synthesized ZnO nanorods was in the range (48.35-56.06) nmUniversity of GarmianPasser Journal of Basic and Applied Sciences270659442220200901Comparison Effects of Soil and Foliar NPK Fertilizers Applications with Various Times on Growth of Rosa Plant (Rosa hybrida L.)768013325910.24271/psr.15ENMariwan AbdulkarimAliDepartment of Horticulature and Landscape Desgin , Bakrajo Technical Instuite , Sulaimani Polytechnic UniversityJournal Article20210708<strong>This experiment was conducted in greenhouse during the 2018-2019 growing season at Horticulture and landscape design department, Technical Institute of Bakrajo, Sulaimani Polytechnic University, Sulaimani, Kurdistan Region/ Iraq. The Rosa plants (</strong><em>Rosa hybrida</em><strong> L.) were used as a test plant .The main objective of the study was to determine the impact of two different ways of NPK (20:20:20) fertilizer applications (soil and foliar), and four different time with five replications per treatment on some of the plant’s growth parameters; Height of plants (HP) cm, Number of Branch / Plant (NBP), Leave Number / Plant (LNP), Number of Flowers / Plant (NFP), Flower Diameters (FD) cm, Number of Buds Remaining (NBR), Number of Petals/Flower(NPF). The different fertilizer application had significant impacts on studied parameters over time, the addition of fertilizer through soil had positive growth impacts in compare with foliar one. However, the foliar application increased some plant growth characteristics like PH, NBP, NLP, and FD. As the NPK doses applied once / two weeks the plant morphological characteristic increased over the control. The best interaction among the Soil and Foliar fertilizer, and different time NPK application was a combination of the NPK applied directly from the soil once / two weeks.</strong>University of GarmianPasser Journal of Basic and Applied Sciences270659442220200901Supervised Sentiment Analysis Model of Textual Content for Images818613325810.24271/psr.16ENWrya AnwarHayderDepartment of IT , College of Computer & IT, University of Garmian, Kalar, Kurdistan Region, IraqJournal Article20210708<strong>Sentiment analysis is a domain in machine learning that tries to analyze people’s emotion, feeling, opinion and attitudes towards particular service or product. It aims to extract feelings and opinion from textual reviews; therefore, it is closely related to natural language processing (NLP). Social media has provided a huge amount of text reviews, which is practically impossible to read and analyze the emotions, attitudes and opinions that were expressed in those textual data. Sentiment analysis is a machine learning concept to classify a textual data according to reviewers’ emotion and attitudes about a service or product, which helps in determine strong or weak production. In this paper work we aim to develop a sentiment analysis model of texts for images. Different machine learning algorithms are tested such as Naive Bays, Logistic Regression and Support Vector Machine (SVM), in order to develop a high accuracy sentiment analysis system. The model is developed to determine whether a text has positive or negative emotion for images. The outcome of the project work shows that SVM algorithm has a better performance for such purpose, while Logistic Regression algorithm shows a faster execution time.</strong>