ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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373 research outputs found
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Analyzing Colorectal Cancer at the Molecular Level through Next-generation Sequencing in Erbil City
Full text linkColorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths globally. It is characterized as a genomic disorder marked by diverse genomic anomalies, including point mutations, genomic rearrangements, gene fusions, and alterations in chromosomal copy numbers. This research aims to identify previously undisclosed genetic variants associated with an increased risk of CRC by employing next-generation sequencing technology. Genomic DNA was extracted from blood specimens of five CRC patients. The sequencing data of the samples are utilized for variant identification. In addition, the Integrative Genomic Viewer software (IGV) is used to visualize the identified variants. Furthermore, various in silico tools, including Mutation Taster and Align GVGD, are used to predict the potential impact of mutations on structural features and protein function. Based on the findings of this research, 12 different genetic variations are detected among individuals with CRC. Inherited variations are located within the following genes: MSH6, MSH2, PTPRJ, PMS2, TP53, BRAF, APC, and PIK3CA
Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell
Full text linkIn this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells
Design and Fabrication of a Microstrip Low-Pass Filter with a Wide Stopband Using a Windmill-Shaped Resonator
Full text linkIn this paper, a microstrip low-pass filter (LPF) with a wide stopband and a sharp transition band is presented using a windmill-shaped resonator. Traditional LPF designs often face challenges such as narrow stopbands, high insertion loss, and large physical sizes, which limit their performance in modern communication systems. To address these challenges, the proposed filter exhibits low insertion loss, a sharp response in the transition band, a wide stopband, and a compact size. The windmill-shaped resonator is applied to achieve a sharp response in the transition band, while two suppressor cells are added to extend the stopband. The filter has a 3 dB cutoff frequency (fc) of 1.61 GHz, with an S12 parameter value of −20 dB at 1.7 GHz, resulting in a narrow transition band of 0.18 GHz, demonstrating its superior performance. In addition, the filter achieves a wide stopband that extends from 1.79 GHz to 11.26 GHz (a bandwidth of 9.47 GHz) with high attenuation. The physical size of the filter is 13.34 mm × 12.78 mm (0.097λ × 0.093λ). Overall, the proposed filter demonstrates excellent characteristics in both the passband and stopband regions, providing an effective solution for modern communication system requirements. The presented design effectively addresses key limitations in traditional LPF configurations, offering improved performance and compactness
Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model
Full text linkThe brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC
Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression
Full text linkKerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq;
therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines
Geotechnical Assessment of the Slopes of Hamamok Dam, NE of Koya, Kurdistan Region of Iraq
Full text linkThe Hamamok Dam is an earthfill dam with a height of 25 m and length of 125 m; constructed in 2011; located northwest of Koya town on a deep canyon-like valley that flows along the southeastern plunge of the Bana Bawi anticline; which forms Bawagi Mountain. The exposed rocks in the site belong to the Pila Spi and Gercus formations; however, rocks of the Khurmala and Kolosh formations are exposed upstream from the dam’s reservoir. The difference in the hardness of the carbonate rocks of the Pila Spi Formation which forms the uppermost parts of the cliffs surrounding the dam site and those of soft reddish brown clastics of the Gercus Formation caused steep slopes that suffer from slope instability problems. To assess a geotechnical study of slopes in the dam site, we have collected different field data to perform a Kinematic assessment method using Dip Analyst 2.0 software and draw the stereographic projection for the studied 10 stations using Stereonet v.11 software. Besides Bejerman’s method, which is based on field data only and indicates the Landslide Possibility Index (LPI). The results showed that the L.P.I. values range between 23 and 27, whereas the results of the Kinematic analysis showed that the right bank (Stations 1 – 5) suffers from plane sliding, whereas the left bank (Stations 6 – 10) suffers from toppling. In both cases, Joint 2 has the main role in the developed failures
Transmission Power Reduction Based on an Enhanced Particle Swarm Optimization Algorithm in Wireless Sensor Network for Internet of Things
Full text linkA wireless sensor network (WSN) consists of several sensor nodes; all these nodes can sense physical events, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, swelling with the concept of the Internet of Things. However, WSN needs to address the challenges of energy issues; thus, energy-conserving techniques have been pursued for communication. Optimization of energy is normally solved using the Particle Swarm Optimization (PSO) algorithm since it offers high accuracy but is prone to local optima, thus resulting in early convergence. To tackle this challenge, this paper proposes the development of an enhanced particle swarm optimization for the node power estimation (EPSO-NPE) model. EPSO-NPE calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals superior performance by EPSO-NPE. The enhanced model exhibits heightened energy-saving capabilities, ultimately extending the network’s lifetime
Strategies for Sustainable Water Management: Hydrochemical Profiling and Protection Zone Design in Rania Basin, Iraq
Full text linkGroundwater in the Rania basin, Iraqi Kurdistan region, has been under intensive exploitation in the last two decades, where quantity and quality are both affected. Hence, any attempt to protect the aquifers has become an urgent need. Saruchawa, Qulai Rania, and Qulai Kanimaran are the three large springs, among dozens of others in the area, that are heavily relied on as the sole or main source of water supply. Hydrochemical analysis, the first and most practical step to evaluating the water quality, was carried out through 60 water samples collected from 13 springs and 17 wells in both dry and wet seasons (October 2018 and May 2019). Laboratory results show a high calcium bicarbonate concentration with weak acids’ dominance. Protection zones are delineated for these springs using aquifer susceptibility to contamination and analysis of the recession part of the spring curves. The equivalent relationship between the protection factor (Fp) produced by the Epikarst, protective cover, infiltration condition, and Karst network development mapping method and the groundwater protection zone (S) is considered. Qulai Rania and Kanimaran Springs are mapped to be in S2 (a highly vulnerable area), whereas Saruchawa Spring is located in S1 (very highly vulnerable). Based on the second method results (recession curve analyses), all three selected springs fall under the (D-type) vulnerability category. As a result, the immediate protection zone was going to be surrounded by the inner protection zone, and both are enclosed within the outer protection zone, which covers the remainder of the catchment area
Dual Electrochemical Methods for Determination of Anesthetic Procaine: Square Wave Voltammetry and Differential Pulse Polarography
Full text linkProcaine belongs to a type of medicine in which excessive dosage form creates cardiac problems and many allergenic reactions. Thus, continuous monitoring of this drug and its metabolite is crucial for sustainable health management during treatment. In this study, electrochemical techniques such as square wave voltammetry (SWV) and differential pulse polarography (DPP) are utilized for assaying procaine amounts in standard and pharmaceutical formulations. In SWV, the reduction of diazotized procaine gives a reduction peak at −0.05 V which is directly proportional with procaine hydrochloride concentration, whereas in DPP, the interaction of the drug with lead cation at −0.4 V is followed by the decrease in peak current of the lead cation reduction peak, which is directly proportional with the concentration of the drug. Both methods indicate high accuracy, sensitivity and precision. Linear concentration ranges of both methods are 0.0999–5.996 × 10-7 M for SWV and 0.1999–5.996 × 10-7 M for DPP. The limit of detection (LOD) and limit of quantification (LOQ) are calculated for both SWV and DPP techniques, and found that LOD equals 1.984 × 10-9 M and LOQ equals 6.611 × 10-9 M for SWV, while for (DPP) LOD and LOQ were found to be 3.519 × 10-9 M and 1.173 × 10-8 M, respectively
Surveillance of Antimicrobial Resistance in Iraq: A Comprehensive Data Collection Approach
Full text linkAntimicrobial resistance (AMR) generates serious negative impacts on health-care systems worldwide, and Iraq is not an exception. To uncover the prevalence of AMR and to visualize the magnitude of the multidrug-resistant (MDR) dilemma in Iraqi hospitals, this study is carried out. A total of 11592 clinical records from ten different health-care facilities in seven Iraqi provinces are collected and analyzed. Our data show that 4984 (43.0%) of all clinical samples are negative for bacterial growth. In adults, Gram-negative bacteria (GNB) represented 48.9% and Gram-positive bacteria (GPB) represented 51.1% of clinical isolates; in children, GNB represented 60.8% and GPB represented 39.2%. Furthermore, in adults, Klebsiella pneumoniae (30.1%) and Staphylococcus aureus (40.8%) are among the most common GNB and GPB isolates, respectively. In children, K. pneumoniae (37.9%) and Staphylococcus haemolyticus (41.8%) are the most common GNB and GPB, respectively. Adults’ samples showed that Escherichia coli and Proteus mirabilis were the most resistant GNB; S. aureus and Staphylococcus epidermidis are among the most resistant GPB. In children, K. pneumoniae is found to be the most resistant GNB. This study confirms the persistence of antimicrobial resistance and multidrug-resistant gram-negative and gram-positive bacteria in adults and children alike. Ampicillin and oxacillin have been recognized as ineffective drugs in adults, and ampicillin, nafcillin, cefoxitin, and benzylpenicillin have been found to be highly resisted by pathogenic bacteria in children. The outcomes confirm the necessity of conducting AMR surveillance on a regular basis and establishing national antibiotic prescription guidelines to manage AMR development in Iraq