ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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373 research outputs found
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Influence of Tetramethylammonium Hydroxide Cation Concentration on Omega Zeolite Crystal Size
Full text linkOmega zeolite nanocrystals can be synthesized hydrothermally from a sodium aluminosilicate solution characterized by a 5.96 Na2O/Al2O3 constant molar ratio, carried out at a maximum temperature of about 100°C for 4 days after aging at room temperature for 3 days, utilizing tetramethylammonium hydroxide (TMA-OH) at molar ratios of 0.36, 0.48, and 0.61. By using different analysis techniques, such as X-ray diffraction, energy dispersive X-ray spectroscopy, scanning electron microscopy, and atomic force microscopy, the physical characteristics of the nanosized omega zeolite crystals can be identified, and the omega zeolite crystal size can be regulated between 34 and 100 nm. In this research paper, the process of creating a uniform aluminosilicate solution with TMA-OH, followed by forming a solid aluminosilicate gel with adjusted elemental composition, reveals the significance of the TMA-OH/Al2O3 mole ratio for synthesizing nanocrystalline omega zeolite aggregates
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Full text linkPneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings
A Study of Large Language Models in Detecting Python Code Violations
Full text linkAdhering to good coding practices is critical for enhancing a software’s readability, maintainability, and reliability. Common static code analysis tools for Python, such as Pylint and Flake8, are widely used to enforce code quality by detecting coding violations without executing the code. Yet, they often fail to handle deeper semantic understanding and contextual reasoning. This study investigates the effectiveness of large language models (LLMs) compared to traditional static code analysis tools in detecting Python coding violations. Six state-of-the-art LLMs: ChatGPT, Gemini, Claude Sonnet, DeepSeek, Kimi, and Qwen are evaluated against Pylint and Flake8 tools. To do so, a curated dataset of 75 Python code snippets, annotated with 27 common code violations, is used. In addition, three common prompting strategies: Structural, chain-of-thought, and role-based, are used to instruct the selected LLMs. The experimental results reveal that Claude Sonnet achieved the highest F1-Score (0.81), outperforming Flake8 (0.79) and demonstrating strong precision (0.99) and recall (0.69). However, LLMs show differences in performance, with Qwen and DeepSeek underperforming relative to others. Moreover, LLMs that identified documentation and design violations (such as type hints and nested method structures) perform better than stylistic consistency and complex semantic reasoning. The results are heavily influenced by the prompting approach, with structural prompts yielding the most balanced performance in the majority of cases. This research contributes to the empirical work on employing LLMs for code quality assurance while also demonstrating their potential role as complementary static code analysis tools for Python, with methodologies that may extend to other languages
Estimation of Atherogenic Index of Plasma, Prolactin, and Some Other Hormones in Polycystic Ovarian Syndrome in Lean Women
Full text linkPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with an increase in androgen levels that influences young women during reproductive age, and it is associated with different reproductive health issues and has significant influences on metabolic pathways. This study focused on the estimation of the atherogenic index of plasma (AIP), LDL/ HDL ratio, Prolactin, and some other parameters that influenced lean women with PCOS. A total of 120 women from Shahid Doctor Khalid Hospital (Koya health centers) in Koya-Erbil Governorate, aged from 25 to 45 years, were distributed into 90 lean women patients with PCOS and 30 healthy individuals who have no PCOS as control groups. This work revealed that the parameters of serum Prolactin, LH, FSH, TSH, Testo, insulin, FBG and HOMA-IR were significantly elevated in lean women with PCOS groups as compared with normal groups. The lipid profile parameters of serum T-Chol, TG, LDL, and VLDL were significantly higher in the PCOS group when compared with healthy individuals, whereas the serum level of HDL was significantly decreased in the PCOS group compared to healthy persons. No significant difference was found for serum Mg among PCOS and control groups. This study indicates from the calculation of BMI (kg/m2) that women with PCOS have normal body weight. AIP is detected as a useful biomarker to indicate the cardiovascular disease (CVD) risk in the future. The results of this study indicate that those lean women with PCOS have insulin resistance (IR) due to the high levels of HOMA-IR
Extraction of Nickel Oxide from Spent Catalyst for Environmentally Safe Disposal
Full text linkMolecular sieves are used in various industries, especially petroleum and gas processing plants, as catalysts. These materials are in contact with crude oil products. After several operational years, these materials’ activities were reduced to a nonfeasible level called spent molecular sieve. Tens of tons are disposed of annually from oil and gas companies in Iraq. The paper aims to determine the kinds and amounts of toxic materials carried by the nickel oxide sulfur bed spent catalyst and then submit the suitable treatment methods, such as leaching by water, base solution, and acid solution. Aradioactive test was first done to ensure the material was free from the radioactivity array. The material was tested for nickel oxide concentration after each step of treatment. It was found that the leaching by water reduces the content by 4.5% during 24 h of leaching and 15.5% after 7 days. The leaching by alkaline sodium hydroxide 10% concentration solution reduces the content by 7% during 24 h and 14.3% after 7 days. The 10% hydrochloric acid concentration solution leaching reduces the nickel content by 10.8 during 24 h and 65.7 after 7 days. Leaching by acid solution is more efficient in the extraction of nickel oxide. The treatment method novelty is to be carried out at reasonable temperatures with high metal extraction efficiency. The research results achieved this goal of attaining extraction at an easily achievable temperature of 70°C with a relatively good extraction rate higher than 65%
The Effect of Microwave Irradiation on the Laser-generated Ag-TiO2 Compound Nanoparticles
Full text linkThe pulsed laser ablation technique in liquid solutions is a promising method for generating nanoscale materials due to its chemically clean and simple synthesis process. This study generates spherical Ag-TiO2 compound nanoparticles (CNPs) through pulsed laser ablation, a picosecond (ps) laser, in deionized water. Then the spherical shapes of the CNPs are changed to rod-like shapes using microwave (MW) irradiation in an ordinary MW (continuous- CW) machine at 700 W for 3.5 min. The effect of MWs on the CNPs is investigated. Before and after MW-irradiation, the samples are characterised using ultraviolet-Vis Spectrometer, transmission electron microscope, and scanning electron microscope machines. The results show that the spherical shape of the nanoparticles was changed to rod-like shapes after MW irradiation. Their nominal dimensions range from 50–70 nm to 150–700 nm in width and length, respectively. Changing the morphology of the nanoparticlesis important for various applications
Predicting the Unconfined Compressive Strength of Rice Husk Ash – Treated Fine-grained Soils
Full text linkThis study aims to develop novel and accurate data-driven predictive models to replace labor-intensive laboratory testing for estimating the unconfined compressive strength (UCS) of problematic soils treated with rice husk ash (RHA) Full Quadratic, Interaction, M5P-tree, and Artificial Neural Network (ANN) were trained and evaluated using a dataset of 211 samples that involved seven key geotechnical parameters, including RHA content (0–30%), liquid limit (22–108%), plasticity index (1.3–82%), maximum dry density (1.2–1.9 g/cm3), optimum moisture content (10.5–42.6%), and curing time (CT) (0–112 days). Among all these models, the ANN model demonstrated superior performance (R2 = 0.97, RMSE = 24 kPa, MAE = 17 kPa, SI = 0.10). Sensitivity analysis revealed CT as the most influence factor (21.9%), followed by moisture content (16.1%) and RHA content (15.3%). The findings present that these predictive models provide a hybrid empirical–machine learning approach, and an accurate alternative to traditional UCS testing, significantly reducing the need for laboratory experiments. They also emphasize enhanced geotechnical performance and the sustainable reuse of agricultural waste. Furthermore, the models can offer a time-efficient solution with practical applications in areas such as highway development and foundation engineering
Simulation of Flare Discharged from Oil Fields, Integration of Remote Sensing, Laboratory and Mathematical Models
Full text linkThe prevailing practice in Iraq and the Middle East involves the flaring of gas into the atmosphere by a majority of oil and gas industries. This practice, however, is causing significant harm to the environment, personnel, and equipment. Consequently, determining the optimal location for the flare stack within an oil field has become a primary concern in the design of oil field processes. To address this issue, an in-depth analysis of the flame distribution from oil field flare stacks has been undertaken, focusing on assessing both the size and configuration of the flare. The investigation specifically concentrated on the diffusion of the heat around a flare discharged from a vertically positioned cylindrical pipe into the atmosphere. To facilitate this exploration, a geographic information system was used, and an environmental laboratory experiment was conducted using a scaled flare stack, allowing for measurements under various conditions. During this experiment, thermal images of the flare at different gas flow rates were captured and analyzed using MATLAB software to precisely measure the dimensions and shape of the flare. Consequently, predicting the shape and size of flare profiles becomes possible when key parameters, such as discharge gas flow rate, are known. The overarching objectives of this study are to forecast the shape and size of the flare as well as the diffusion zone, contributing to a more effective and environmentally friendly oil field process design
Enhanced Category-Feature Association Measure: A Robust Approach for Text Classification through Feature Selection
Full text linkText classification is one of the severe challenges for categorizing large and high-dimensional text data accurately and efficiently. Many features confuse the classification process, and feature selection (FS) strategies should be used to deal with the problem of high dimensionality. This paper proposes a novel FS technique based on enhanced category-feature association measure (ECFAM). ECFAM utilizes the existence and elimination of terms and the complicated relationships among the terms across different sections. This one-of-a-kind approach emphasizes the key role of ancillary terms in classifying and differentiating categories. The comparison is done on two important datasets, Reuters-21578 and 20-Newsgroups, through two widely employed supervised machine learning classifiers and one deep learning algorithm. Throughout our experiments, we investigate the feature sizes in nine different feature sets, ranging from 50 to 4000. Experimental data show that ECFAM always performs better than other methods concerning accuracy and computational cost
Evaluation of 3D-CRT Treatment Planning Techniques for Breast Cancer: A Comparative Study of Collapsed Cone and Monte Carlo Algorithms Using New Quality Indices
Full text linkThis study compares the collapsed cone (CC) and Monte Carlo (MC) algorithms for radiation treatment planning for lumpectomy of the chest wall. The aim is to evaluate how these algorithms affect dose distribution and plan quality improve treatment outcomes. Fifteen patients received left breast chest wall radiation using the 3D-conformal radiotherapy (3D-CRT) technique with CC calculation. Then plans were subsequently recalculated using the MC algorithm on the same treatment planning system. Dosimetric parameters assessed included the planning target volume (PTV), homogeneity index (HI), and conformity index. In this research, new plan quality indicators named index of achievement, index of hotness, and index of coldness were also evaluated. Organs at risks (OARs) analyzed included the ipsilateral lung, contralateral breast, heart, and spinal cord, and their data were retrieved from the dose-volume histogram (DVH) and compared among algorithms. The results indicated that both algorithms effectively covered PTV. The MC algorithm improved HI and reduced the DVH high dose to the prescribed dose. Interestingly, the CC algorithm resulted in lower mean dose to OAR, particularly the heart and ipsilateral lung, suggesting better OAR sparing. The new quality indexes, the MC algorithm demonstrated superior “index of achievement” values, indicating improved dose painting and better dose conformity within the target. In addition, the MC showed a sharper dose falloff outside the PTV, thereby improving target coverage and overall plan quality. In conclusion, the MC algorithm provides enhanced dose homogeneity and better target coverage quality, while the CC algorithm offers improved OAR protection