ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
type: channel
description: ARO, which means "Today" in Hewramí Kurdish, is a distinguished scientific journal published by Koya University. It is a diamond open access journal with an electronic ISSN (e-ISSN) of 2307-549X, a print ISSN (p-ISSN) of 2410-9355, and a Digital Object Identifier (DOI) of 10.14500/2307-549X. ARO encompasses a wide range of scholarly contributions, including research articles, review articles, and letters to the editor. ARO is a peer-reviewed journal that promotes scientific and engineering research. It has gained recognition and credibility through its inclusion in the DOAJ and DOAJ Seal. ARO has achieved an Impact Factor of 0.6 (2023) and 2.1 (2024) by WoS-Clarivate Analytics, and SCOPUS indexing in May 2025, demonstrating its reputable status in the scholarly community.
publisher: Koya University
language: en-US
publication name: ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
issn: 2410-9355
copyright: Authors who choose to publish their work with Aro agree to the following terms: Authors retain the copyright to their work and grant the journal the right of first publication. The work is simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0]. This license allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors have the freedom to enter into separate agreements for the non-exclusive distribution of the journal's published version of the work. This includes options such as posting it to an institutional repository or publishing it in a book, as long as proper acknowledgement is given to its initial publication in this journal. Authors are encouraged to share and post their work online, including in institutional repositories or on their personal websites, both prior to and during the submission process. This practice can lead to productive exchanges and increase the visibility and citation of the published work. By agreeing to these terms, authors acknowledge the importance of open access and the benefits it brings to the scholarly community.
items: Deep Learning for Cardiovascular Disease Detection
items: Intelligent Transportation Systems for Deep Learning-Driven Vehicular Ad hoc Network
items: Formalizing Public Transit in Mid-Sized Developing Cities
items: A Systematic Survey on Large Language Models for Code Generation
items: Broadband Metamaterial Absorbers for Organic Solar Cells
items: Cryptocurrency Time Series Forecasting Based on Ensemble and Deep Learning Algorithms
items: Investigating the Relationship Between Insulin-Like Growth Factor-I and Thyroid Hormones in Thyroid Disorder Patients
items: Impact of Cement Kiln Dust on Soil Geotechnical Properties
items: Durability Assessment of Green Concrete Incorporating Volcanic Tuff Pozzolan, Basalt, and Recycled Aggregate
items: A Spatio-Temporal Deep Learning Approach for Efficient Deepfake Video Detection
items: Health Risk Assessment of Heavy Metals in Indoor Dust in Erbil City
items: Impact of Vitamin D Deficiency on Iron Deficiency Anemia
items: Enhanced Category-Feature Association Measure
items: Synthesis, Characterization, and Antioxidant Studies of Novel Cu(II) and Ni(II) Homo Binuclear Complexes with N,N’-bis(Benzamidothiocarbonyl) Hydrazine Ligand Derivatives
items: Evaluation of 3D-CRT Treatment Planning Techniques for Breast Cancer
items: Performance Evaluation of Membrane Bioreactor Operational Design in Sewage Treatment Plant using GPS-X Software
items: A Hybrid Deep Learning Model with Self-Attention for the Classification of Lung Cancer Using Histopathology Image
items: High Reflectivity Compounds of Cadmium Sulfide/Magnesium Fluoride Distribution Bragg Reflectors
items: Influence of Microstructure and Droplet Volume on Atmospheric Pitting Corrosion of 304L Austenitic Stainless Steel
items: Radon in Commercial Cigarettes and Associated Health Risks
items: Influence of Patient Size and Photon Energies on IMRT Plans for Localized Prostate Cancer
items: Wild Cherry (Prunus microcarpa) Ameliorates Azoxymethane-Induced Aberrant Crypt Foci in vivo
items: Dolomitization and Hypogenic Dissolution of the Eocene Avanah Formation, Iraqi Kurdistan
items: A Study of Large Language Models in Detecting Python Code Violations
items: Uric Acid Dynamics in Young Women
items: Estimation of Atherogenic Index of Plasma, Prolactin, and Some Other Hormones in Polycystic Ovarian Syndrome in Lean Women
items: Identification of OPN1LW Exon 3 Variants Impairing Red-Cone Function in Color Vision Deficiency
items: Spatio-Temporal Evaluation of Physicochemical Parameters, Heavy Metals, and Pesticides in the Little Zab River
items: Influence of Structural Properties on Fusion Cross-Sections Using Proximity Potentials
items: Novel Design of a Lowpass-Bandpass Diplexer with an Exceptional Performance Using Microstrip Meandrous Loaded Lines for Microwave Systems
items: Modification of the Optical Properties of Polyvinyl Alcohol through Incorporating Cu2O Nanoparticles Prepared by Laser Ablation
items: Immunomodulatory Pathways of Some Biomarkers in Individuals with Renal Failure
items: Assessment of Corrosion Inhibition Efficiency of Some Amino Acids on Stainless Steel in Ethaline
items: Using Structural Data to Indicate the Folding Type of Anticlines Examples from the Kurdistan Region, Iraq
items: An Ensemble-based Model for Sentiment Analysis of Kurdish Tweets

Deep Learning for Cardiovascular Disease Detection
type: item
description: Despite improvements, cardiovascular diseases (CVD) remain the most significant killer globally, accounting for around 17.9 million lives annually. Advancement of cardiac imaging modalities has taken place with Magnetic Resonance Imaging (MRI) along with artificial intelligence (AI) for changing scenarios of early diagnosis and management in cardiovascular diseases. This work investigates the role and contribution of deep learning, especially Fully Convolutional Networks (FCNs) and Convolutional Neural Networks (CNNs), toward the improvement of accuracy and automation in cardiac MRI analysis. The integration of AI enables accurate segmentation, efficient clinical workflows, and scalable solutions for resource-limited environments. A review of publicly available datasets underlines challenges in data variability and generalizability and points to the need for standardized models and explainable AI approaches. This work, therefore, underlines the possibility of improved diagnostic efficiency and equity in healthcare delivery using AI-driven methodologies in cardiovascular diagnostics. Future directions will focus on refining model scalability, enhancing dataset diversity, and validating clinical applications to foster robust and adaptable solutions.
creator: Shivan H. Hussein
creator: Najdavan A. Kako
rights: Copyright (c) 2025 Shivan H. Hussein, Najdavan A. Kako https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-09
publication date: 2025-07-09
volume: 13
number: 2
starting page: 1
ending page: 17
doi: 10.14500/aro.11971

Intelligent Transportation Systems for Deep Learning-Driven Vehicular Ad hoc Network
type: item
description: Abstract—Numerous studies demonstrate that the vehicular ad hoc network (VANET) depends on various characteristics and intermediate connections. It offers real-time automatic reaction and acute traffic analysis, but more studies are still needed to determine how best to use it in various situations. The primary goals of this VANET system are to distinguish between specific agents and identify collision remnants, which is still a research area in terms of scalability, optimization strategies, and efficient data aggregation. Due to problems with distance disintegration, temporal channel deterioration, and signal distortion, analysis was not feasible until recently. Therefore, this research will carry out a comparative review of available studies related to Intelligent Transportation Systems that use deep learning applications in VANETs, such as the recurrent neural network model, cybersecurity, decision-making, and collision avoidance, as well as future work, so it can have a more concise understanding of the topic.
creator: Ghassan A. QasMarrogy
rights: Copyright (c) 2025 Ghassan A. QasMarrogy https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-27
publication date: 2025-07-27
volume: 13
number: 2
starting page: 26
ending page: 36
doi: 10.14500/aro.12054

Formalizing Public Transit in Mid-Sized Developing Cities
type: item
description: Mid-sized cities in developing cities face increasing demand to modernize their public transit (PT) systems to advance sustainability, equity, and resilience. Many of these cities remain dependent on informal transit modes such as minibuses, privately owned taxis, and shared vans which, despite their flexibility, often lead to operational inefficiencies, safety risks, and limited accessibility. This review examines strategies for transitioning to formal public bus transit (BT) systems through analysis of peer-reviewed literature. The analysis is organized around five core domains that directly reflect the structure of this study: assessment of the current state of PT systems, strategies for transitioning from informal to formal networks, selection of appropriate PT modes for mid-sized cities, planning processes for BT systems, and sustainable and resilient approaches for BT development. Based on these findings, this study proposes a structured decision-support framework in the form of a decision tree to guide context-sensitive formalization efforts. Future studies should prioritize long-term impact evaluation, inclusive transition mechanisms for informal operators, and the integration of smart and sustainable technologies.
creator: Hasan Shahab
creator: Hemin Mohammed
rights: Copyright (c) 2025 Hasan Shahab, Hemin J. Mohammed https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-29
publication date: 2025-07-29
volume: 13
number: 2
starting page: 45
ending page: 63
doi: 10.14500/aro.12185

A Systematic Survey on Large Language Models for Code Generation
type: item
description: The rapid development of large language models (LLMs) has transformed code generation, offering powerful tools for automating software development tasks. However, evaluating generated code’s quality, security, and effectiveness remains a significant challenge. The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation process. The survey explored ten research questions, such as the most commonly used programming languages, the metrics employed to evaluate the quality of code, and scenarios in which LLMs are applied by developers during the software development process, outlining the scope in which prompt engineering influences code generation and security concerns with the types of benchmarks, models evaluated, and code analysis tools used in studies. The findings indicate that the most frequently used evaluation metrics in code generation are Pass@k and Bilingual Evaluation Understudy. It also shows that Python, Java, and C++ are the most widely used languages. Furthermore, identifying security vulnerabilities and establishing robust evaluation metrics remain challenges. This survey underlines present practices, detects gaps, and suggests future research to enhance the reliability and security of code generated by LLMs in real-world applications.
creator: Sardar K. Jabrw
creator: Qusay I. Sarhan
rights: Copyright (c) 2025 Sardar K. Jabrw, Qusay I. Alsarhan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-06
publication date: 2025-08-06
volume: 13
number: 2
starting page: 83
ending page: 99
doi: 10.14500/aro.12159

Broadband Metamaterial Absorbers for Organic Solar Cells
type: item
description: Organic solar cells (OSCs) often suffer from weak absorption in the visible and infrared spectrum, which directly restricts their efficiency. Since light harvesting is central to solar conversion, improving absorption across a broad range is critical. Broadband metamaterial absorbers (BMMAs) present a promising solution by enhancing light-matter interaction and extending absorption over a broader spectrum. This improvement directly makes the process of converting energy more efficient. This review aims to systematically examine the recent progress of metamaterial absorbers (MMAs), highlighting broadband, polarization independent, and wide-angle designs areas that remain unexplored in recent reviews. Different categories of strategies, such as planar, vertical, lumped-element, and nanostructured plasmonic designs, are discussed to highlight how material choice and design geometry affect absorption. In addition, it describes the physical concepts of perfect absorption and assesses how applicable they are to OSC integration. Our analysis shows that the most of the progress has been theoretical approaches with a limited experiment. These studies demonstrate that BMMAs have an excellent opportunity to significantly improve energy conversion efficiency. At the same time, most challenges remain, like scalability, material losses, and easy integration into OGCs. This research also points out that there are future investigations into affordable, low-loss materials that can be easily integrated. Overall, this study emphasizes how important MMBAs are to advancing the efficiency and sustainability of next generation OSCs.
creator: Diyaree O. Kakil
creator: Fahmi F. Muhammadsharif
creator: Yadgar I. Abdulkarim
rights: Copyright (c) 2025 Diyaree O. Kakil, Fahmi F. Muhammadsharif, Yadgar I. Abdulkarim https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-06
publication date: 2025-11-06
volume: 13
number: 2
starting page: 233
ending page: 255
doi: 10.14500/aro.12025

Cryptocurrency Time Series Forecasting Based on Ensemble and Deep Learning Algorithms
type: item
description: Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
creator: Hilmi S. Abdullah
creator: Adnan M. Abdulazeez
rights: Copyright (c) 2025 Hilmi S. Abdullah, Adnan M. Abdulazeez https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-15
publication date: 2025-12-15
volume: 13
number: 2
starting page: 359
ending page: 366
doi: 10.14500/aro.12280

Investigating the Relationship Between Insulin-Like Growth Factor-I and Thyroid Hormones in Thyroid Disorder Patients
type: item
description: The insulin-like growth factor I (IGF-I) system plays a pivotal role in endocrine regulation, metabolism, and cellular growth; however, its interaction with thyroid hormones in the context of thyroid dysfunction remains insufficiently understood. This study aimed to investigate the correlations between IGF-I and thyroid hormones, TSH, T3, and T4, in females diagnosed with thyroid disorders. After removing 15 women due to pregnancy or medication use, 160 women from the Koya district were assessed. Serum levels of IGF-I and thyroid hormones were measured using the COBAS e411 analyzer, and body mass index (BMI) was also recorded. Statistical analysis was conducted using SPSS 25, employing Kruskal-Wallis ANOVA and Spearman correlation for thyroid groups: hyperthyroid, hypothyroid, and euthyroid. The results demonstrated significant variation in IGF-I levels across thyroid conditions. IGF-I was markedly elevated in hyperthyroid individuals and showed a moderate inverse correlation with TSH, along with a positive correlation with T3 and T4. Hypothyroid individuals exhibited significantly lower IGF-I levels, suggesting a regulatory role of IGF-I in thyroid hormone dynamics. Additionally, BMI varied significantly across groups (p < 0.001), with the highest values are observed in hypothyroid participants, supporting the metabolic implications of thyroid dysfunction and its association with IGF-I. The study concludes that IGF-I could serve as a valuable adjunct biomarker in the assessment of thyroid disorders, particularly in ambiguous or subclinical cases. Incorporating IGF-I into routine thyroid evaluation may enhance early detection, risk stratification, and individualized management strategies, contributing to more precise and effective endocrine care.
creator: Aveen M. Asaad
creator: Ismail S. Kakey
rights: Copyright (c) 2025 Aveen M. Asaad, Ismail S. Kakey https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-20
publication date: 2025-07-20
volume: 13
number: 2
starting page: 18
ending page: 25
doi: 10.14500/aro.12181

Impact of Cement Kiln Dust on Soil Geotechnical Properties
type: item
description: As cement demand rises daily; kiln dust from cement-producing facilities is being collected in large quantities. This tiny dust poses a risk to the environment when it is disposed of. This study investigates the potential reuse of CKD as a stabilizing agent for expansive soils. The amount of CKD added to intact soil was 0%, 10%, 20%, 30%, and 40% of the dry mass of the soil. The liquid limit (LL) of the intact soil decreased after adding CKD from 47.7% to 43.7%, and the plastic limit (PL) from 26.97% decreased to 21.36%. A sharp decrease was observed in the consistency index (Ic) and toughness index (It), while the flow index (If), liquidity index (Li) and shrinkage index (Is) are noticeably increased. The unconfined compressive strength (qu), modulus of elasticity (E), and Resilient Modulus (Ur) increased for the samples cured for 7 days and 28 days; the rate of increase in the values of these parameters was observed to be higher in 28 days during the period compared to 7 days. Also, the shear strength and cohesion sharply increased after the soil was treated with CKD. The UPV also increased for both sets of prepared cylindrical soil samples. The CKD additives decreased the hydraulic conductivity of the soil and successfully improved the geotechnical characteristics of fine-grained soil. Thus, CKD can be repurposed as a sustainable soil stabilizer for slopes, subgrades, and foundations, providing a viable solution for its disposal and reducing environmental impact.
creator: Rozhgar A. Hasan
creator: Fahmy O. Mohammed
creator: Nihad B. Salih
rights: Copyright (c) 2025 Rozhgar A. Hasan , Fahmy O. Mohammed, Nihad B. Salih https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-28
publication date: 2025-07-28
volume: 13
number: 2
starting page: 37
ending page: 44
doi: 10.14500/aro.11961

Durability Assessment of Green Concrete Incorporating Volcanic Tuff Pozzolan, Basalt, and Recycled Aggregate
type: item
description: Extending the lifespan of building structures is a critical strategy for mitigating environmental impacts, particularly greenhouse gas emissions from cement production, like emissions from cement. Green concrete, made from pozzolana, basalt, and recycled materials, was tested for durability. Samples were immersed in 2% sulfuric acid for a week, then checked for resistance loss. This study investigates the performance of various concrete mixtures through experiments and simulations where Portland Cement was replaced by natural pozzolana ground into the bonding paste in proportions ranging from 10% to 50% with the use of four types of gravel structures I (natural gravel), II (recycled gravel), III (pozzolanic gravel with basalt sand), and IV (pozzolanic gravel and sand), where 128 cubes were poured with dimensions (10 × 10 × 10) cm to perform simple pressure tests on samples before and after immersion in a solution of sulfuric acid. The results showed that higher cement replacement percentages in mixtures with recycled aggregates resulted in greater durability reduction, with resistance losses exceeding 34% at 50% replacement, primarily due to the rounded aggregate morphology and lower acid resistance. In contrast, mixtures incorporating pozzolanic gravel and basalt sand showed superior performance, achieving only 18.7% resistance loss at 50% replacement (compared to 30.1% at lower replacement rates), highlighting basalt’s effective pore-filling capability. The optimal performance was observed in pozzolanic gravel-sand blends, which exhibited just 13.7% resistance loss, demonstrating enhanced synergistic pozzolanic activity. These findings validate that optimized pozzolana-basalt combinations significantly improve chemical resistance, offering promising solutions for sustainable concrete development.
creator: Moustafa A. Wassouf
creator: Jamal Y. Omran
creator: Ali I. Kheirbek
rights: Copyright (c) 2025 Moustafa A. Wassouf, Jamal Y. Omran, Ali I. Kheirbek https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-01
publication date: 2025-08-01
volume: 13
number: 2
starting page: 64
ending page: 74
doi: 10.14500/aro.12083

A Spatio-Temporal Deep Learning Approach for Efficient Deepfake Video Detection
type: item
description: Deepfake videos have grown to be a big concern in the modern digital media landscape as they cause difficulties undermining the legitimacy of channels of information and communication. Humans often find it challenging to tell the difference between a fake and a genuine video due to the increasing realism of facial deepfakes. Identification of these misleading materials is the first step in preventing deepfakes from spreading through social media. This work introduces Spatio-temporal Intelligent Deepfake Detector (STIDD), a deep learning system including enhanced spatial and temporal modeling techniques. By means of a pre-trained EfficientNetV2-B0 model, the proposed framework efficiently extracts spatial characteristics from each frame, subsequently, and Bidirectional Long Short-Term Memory layers help to capture temporal relationships from video sequences. We evaluate STIDD on the FaceForensics++ (FF++) dataset encompassing all five manipulation techniques (DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures). The experimental results reveal that STIDD achieved precision, recall, and F1-scores all higher than 0.99 and a final test accuracy of 99.51% on the combined FF++ test set. The results demonstrate that the integration of sophisticated spatial extraction and strong temporal modeling allows STIDD to achieve high detection performance while maintaining computing efficiency at just 0.39 Giga Floating-Point Operations (GFLOPs) per inference.
creator: Raman Z. Khudhur
creator: Marwan A. Mohammed
rights: Copyright (c) 2025 Raman Z. Khudhur, Marwan A. Mohammed https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-05
publication date: 2025-08-05
volume: 13
number: 2
starting page: 75
ending page: 82
doi: 10.14500/aro.12190

Health Risk Assessment of Heavy Metals in Indoor Dust in Erbil City
type: item
description: Indoor dust is a significant source of heavy metal exposure, posing potential health risks to residents. This study investigates the concentration and possible health risks of heavy metals in household dust in Erbil City, Kurdistan Region of Iraq, and assesses the associated health risks. 123 indoor dust samples were collected from various residential areas and analyzed using Energy Dispersive X-ray spectroscopy, an advanced technique that enables precise elemental detection. The metals assessed included aluminum (Al), iron (Fe), titanium (Ti), arsenic (As), chromium (Cr), nickel (Ni), cadmium (Cd), and lead (Pb). The highest mean concentrations were observed for aluminum (Al), particularly in the Northwest region (29209 mg/kg), while lead (Pb) exhibited the lowest concentration in the same Northwest region (28 mg/kg). Notably, elevated levels of titanium (Ti) were detected in the northern area, and cadmium (Cd) was most pronounced in the Northeast, reflecting spatial variation potentially linked to traffic emissions, construction activity, and indoor smoking. To evaluate the potential health implications, hazard quotients and hazard indices (HI) are calculated for both children and adults, focusing on non-carcinogenic risks through inhalation exposure in accordance with the United States Environmental Protection Agency guidelines. Although localized HQ values were higher for certain elements, such as Ti and Cd, all calculated HI values remained below the threshold value of 1. This indicates that under current environmental conditions, indoor dust in Erbil City is unlikely to pose significant non-carcinogenic health risks through inhalation.
creator: Nadia M. Shami
creator: Sardar P. Yaba
rights: Copyright (c) 2025 Nadia M. Shami, Sardar P. Yaba https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-09
publication date: 2025-08-09
volume: 13
number: 2
starting page: 100
ending page: 107
doi: 10.14500/aro.12215

Impact of Vitamin D Deficiency on Iron Deficiency Anemia
type: item
description: Two important health problems are iron deficiency anemia and vitamin D deficiency, as well as the ability to cope with acute and chronic diseases, because iron and vitamin D are the main elements of physiological functions in the human body. Recent studies have indicated that generalized IDA in both healthy and diseased populations may be associated with inadequate levels of VD. The research comprised 132 participants, 65 with IDA and 67 controls, the study was conducted in a competent cross-sectional design, between the two groups matched by age and gender. The laboratory findings included the study of blood indices. Ferritin, iron, hemoglobin, mean corpuscular volume, red cell distribution width, and VD levels were measured for each participant. Iron metabolism markers showed highly significant variations between the groups. Patients with IDA exhibited considerably lower levels of iron indicators than the healthy control group, except for total iron binding capacity (TIBC), which was increased among the patients. The normal control group showed substantially higher serum VD levels than patients with iron deficient anemia (p < 0.035). This distinction suggests a high positive link between VD levels and iron metabolism markers, except for TIBC, which exhibited a negative correlation. The results showed a significant correlation between VD levels and several iron metabolism markers in the research participants. This suggests that VD may affect how iron is metabolized and help treat IDA. To fully understand the underlying mechanisms and any therapeutic benefit, further research is needed.
creator: Sleman Y. Omar
creator: Awat H. Awla
creator: Musher I. Salih
creator: Badinan J. Hamadamin
rights: Copyright (c) 2025 Sleman Y. Omar, Awat H. Awla, Musher I. Salih, Badinan J. Hamadamin https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-09
publication date: 2025-08-09
volume: 13
number: 2
starting page: 108
ending page: 113
doi: 10.14500/aro.11980

Enhanced Category-Feature Association Measure
type: item
description: Text 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.
creator: Soran S. Badawi
creator: Ari M. Saeed
creator: Sara A. Ahmed
creator: Diyari A. Hassan
rights: Copyright (c) 2025 Soran S. Badawi, Ari M. Saeed, Sara A. Ahmed, Diyari A. Hassan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 13
number: 2
starting page: 114
ending page: 123
doi: 10.14500/aro.12034

Synthesis, Characterization, and Antioxidant Studies of Novel Cu(II) and Ni(II) Homo Binuclear Complexes with N,N’-bis(Benzamidothiocarbonyl) Hydrazine Ligand Derivatives
type: item
description: This study presents the synthesis, spectral characterization, and antioxidant evaluation of Cu(II) and Ni(II) homo binuclear complexes derived from three bis(thiourea)hydrazine ligands: N, N′-bis(benzamidothiocarbonyl)hydrazine (L1), N, N′-bis(o-chlorobenzamidothiocarbonyl)-hydrazine (L2), and N, N′-bis(p-methylbenzamidothiocarbonyl)hydrazine (L3). The ligands were synthesized via condensation reactions and characterized using CHNS elemental analysis, FT-IR, UV-Vis, and ¹H NMR and ¹³C NMR spectroscopy. The metal complexes were prepared in a 2:1 metal-to-ligand molar ratio and characterized by melting point determination, magnetic susceptibility, molar conductivity, and spectral techniques. IR data confirmed coordination through the thiocarbonyl sulfur and amide oxygen atoms, forming neutral bidentate complexes. Magnetic moment values and electronic spectra were consistent with square planar geometries for both Cu(II) and Ni(II) complexes. Molar conductance measurements indicated non-electrolytic behavior in DMF. Antioxidant activity was assessed via the DPPH radical scavenging assay, revealing that the metal complexes exhibited enhanced radical scavenging capacity compared to the free ligands, likely due to increased delocalization and metal ion involvement in electron transfer. These findings highlight the potential of bis(thiourea)hydrazine-based metal complexes as antioxidant agents.
creator: Huner S. Abdulmanaf
creator: Bashdar I. Meena
rights: Copyright (c) 2025 Huner S. Abdulmanaf, Bashdar I. Meena https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 13
number: 2
starting page: 124
ending page: 133
doi: 10.14500/aro.12284

Evaluation of 3D-CRT Treatment Planning Techniques for Breast Cancer
type: item
description: This 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.
creator: Riyadh S. Agid
creator: Fatiheea F. Hassan
rights: Copyright (c) 2025 Riyadh S. Agid, Fatiheea F. Hassan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-22
publication date: 2025-08-22
volume: 13
number: 2
starting page: 134
ending page: 141
doi: 10.14500/aro.12195

Performance Evaluation of Membrane Bioreactor Operational Design in Sewage Treatment Plant using GPS-X Software
type: item
description: This study evaluates the operational performance of a full-scale membrane bioreactor wastewater treatment plant located in Kuala Lumpur using GPS-X 8.0 simulation software. Key performance indicators–including mixed liquor suspended solids (MLSS), transmembrane pressure (TMP), dissolved oxygen, and effluent total suspended solids (TSS)were monitored over 30 days. The simulations were conducted using the advanced mode and calibrated with actual plant data. Results show that although the plant complies with Malaysian effluent discharge standards, it operates well below its design capacity, with MLSS levels significantly lower than recommended. This operational underload contributes to increased energy consumption and reduced treatment efficiency, particularly in terms of TSS and chemical oxygen demand removal.
creator: Ali S. Dawood
creator: Fatehah M. Omar
creator: Amera G. Baker
creator: Hawbash H. Karim
rights: Copyright (c) 2025 Ali S. Dawood, Fatehah M. Omar, Amera G. Baker, Hawbash H. Karim https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-25
publication date: 2025-08-25
volume: 13
number: 2
starting page: 142
ending page: 151
doi: 10.14500/aro.12033

A Hybrid Deep Learning Model with Self-Attention for the Classification of Lung Cancer Using Histopathology Image
type: item
description: Lung cancer remains a prevalent health burden and is one of the leading causes of cancer mortality worldwide. Its high mortality rate is partly attributable to histological heterogeneity and the difficulty of detecting it at early stages. An accurate distinction of lung cancer subtypes in histopathological images is crucial for improving the accuracy of diagnosis and planning an appropriate treatment to improve the quality of life of patients. This study proposes a hybrid deep-learning model for classifying cancer types using histopathology images. The ConvNeXt-Tiny is an extension of the ResNet-50 base architecture. This architecture is inspired by both models and introduces self-attention layers to improve both feature extraction and classification performance, leading to a unique model design. The proposed model and two other deep learning models were trained and tested using the public Lung and Colon Cancer Histopathological Image (LC25000) dataset and a private clinical dataset, and their effectiveness was evaluated. The proposed model outperformed the best classification accuracy among the other architectures (98.73% public and 93.17% private), outperforming baseline models, such as ConvNeXt-Tiny (96.27% public and 89.33% private) and ResNet-50 (94.00% public and 87.67% private). The results confirm the robustness and generalization ability of the proposed architecture.
creator: Lana L. Nahmatwlla
creator: Abbas M. Ali
rights: Copyright (c) 2025 Lana L. Nahmatwlla, Abbas M. Ali https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-01
publication date: 2025-09-01
volume: 13
number: 2
starting page: 152
ending page: 159
doi: 10.14500/aro.12355

High Reflectivity Compounds of Cadmium Sulfide/Magnesium Fluoride Distribution Bragg Reflectors
type: item
description: This work investigates the optical performance of a distributed Bragg reflectors (DBRs) structure, designed with a Matlab program using inorganic compounds of cadmium sulfide (CdS) and magnesium fluoride (MgF2 ) with a central operational wavelength of 650 nm. Because CdS (high index) and MgF2 (low index) have very different refractive indices, the proposed DBR can reflect light effectively using fewer alternating layers. The Transfer Matrix Method simulation method indicates that the DBR structure reaches its maximum reflectivity with just six-layer pairs, emphasizing its optical efficiency and structural simplicity. A comparative analysis with other DBR structures demonstrates the superior performance of CdS/MgF2 DBR, which exhibits a broader usable stopband at around 181.82 nm, the highest bandwidth of 298.49 nm, and a relatively moderate Q-factor (2.18), indicative of an enhanced reflector response. These results establish CdS/MgF2 DBR as highly efficient reflectors that are well-suited for optical systems functioning in the visible spectrum.
creator: Jihad A. Swara
creator: Faten A. Chaqmaqchee
creator: Khalid N. Sediq
rights: Copyright (c) 2025 Jihad A. Swara, Faten A. Chaqmaqchee, Khalid N. Sediq https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-05
publication date: 2025-09-05
volume: 13
number: 2
starting page: 160
ending page: 166
doi: 10.14500/aro.12258

Influence of Microstructure and Droplet Volume on Atmospheric Pitting Corrosion of 304L Austenitic Stainless Steel
type: item
description: This research investigates the atmospheric pitting corrosion behavior of 304L austenitic stainless steel subjected to MgCl2 droplets, emphasizing the effects of microstructure and droplet volume. X-ray diffraction and scanning electron microscopy (SEM) show that both austenite and ferrite are present, and it is observed that the ferrite bands dissolved more in the direction the steel is rolled. SEM-energy-dispersive X-ray spectroscopy analysis identified mixed oxides and MnS inclusions. The shape of the pits changed depending on the direction of the plate: Layered pits mostly occurred on the longitudinal–transverse side, while striped pits are seen on the longitudinal–short transverse and short transverse sides, indicating variations in the material’s structure. An increase in droplet volume from 0.5 µL to 2.5 µL led to a linear rise in total pit area and a measurable increase in pit depth. These findings show that the direction of the microstructure and the size of the droplets significantly affect how likely pitting is to occur, which is important for designing and using stainless steels in environments with a lot of chloride.
creator: Sarkat R. Hussein
creator: Haval B. Mohammed-Ali
rights: Copyright (c) 2025 Sarkat R. Hussein, Haval B. Mohammed-Ali https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-18
publication date: 2025-09-18
volume: 13
number: 2
starting page: 167
ending page: 174
doi: 10.14500/aro.12445

Radon in Commercial Cigarettes and Associated Health Risks
type: item
description: This study evaluates radon concentrations and associated health risks in 40 commercially available cigarette brands from the Kurdistan Region. Two measurement techniques are employed: RAD-7 (active method) and CR-39 (passive method). Using the CR-39 detector, radon concentrations ranged from 101.99 ± 10.1 Bq·m⁻3 in the S6 (Cigaronne) sample to 339.98 ± 18.44 Bq·m⁻3 in the S32 (Gauloises Gold) sample, with an average concentration of 190.84 ± 13.67 Bq·m⁻3. The RAD-7 measurements show values between 96.01 ± 9.8 Bq·m⁻3 and 282.72 ± 16.81 Bq·m⁻3 in the same samples, averaging 180.30 ± 13.00 Bq·m⁻3. A strong correlation (R² = 0.9271) is observed between the two methods, confirming the reliability of the results. The effective radium content in all cigarette samples remains below internationally recommended safety limits, with Gauloises Gold showing the highest levels. Estimated annual effective doses ranged from 2.57 ± 1.61 mSv·y−1 to 8.58 mSv·y−1, remaining within acceptable limits established by the International Commission on Radiological Protection. Lung cancer risk due to radon exposure varies among brands, with an average of 208 ± 14.27 cases per million individuals. A significant correlation is found between ²²²Rn concentration and estimated annual lung cancer incidence. These findings highlight radon exposure from cigarette smoke as a contributing risk factor for lung cancer, underscoring the need for public health awareness regarding the radiological hazards of smoking.
creator: Sardar Q. Othman
rights: Copyright (c) 2025 Sardar Q. Othman https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-21
publication date: 2025-09-21
volume: 13
number: 2
starting page: 175
ending page: 184
doi: 10.14500/aro.12374

Influence of Patient Size and Photon Energies on IMRT Plans for Localized Prostate Cancer
type: item
description: The localized prostate cancer treatment with intensitymodulated radiation therapy (IMRT) produces precise radiation doses that protect healthy tissues but continues to generate toxicity in the bladder and rectum. Our study aims to evaluate how patient size and photon energy influence IMRT plan quality in localized prostate cancer. Patients with localized prostate cancer (n = 20) received IMRT treatment in a retrospective analysis through two groups (small n = 12 and large n = 8) based on planning target volume isocenter diameter measurements. The same planning objectives guided the build of 6 MV and 10 MV photon plans for each patient. The assessment included monitor units (MUs) as well as examination of homogeneity index and conformity index, and dose–volume histogram metrics when comparing different energies and patient size groups. The target coverage and dose uniformity between 6 MV and 10 MV were equivalent, but 10 MV plans cut down MUs by 6.6% in a small group. The size of patients played a stronger influence on organs at risk (OAR) than energy utilization during treatment planning. Large patients received 22% lower mean bladder doses, 30% lower bladder V10–V50 values, and 3396% (6 MV) less V20–V50 doses in the left femoral head compared to smaller patients. The size of the patient influenced OAR sparing more than the selection of photon energy, which had limited and size‐dependent effects. Therefore, patient size has to be considered in energy choice, treatment planning, and clinical trial design to individualize treatment and minimize toxicity.
creator: Khabat A. Mohammed
creator: Kharman A. Faraj
rights: Copyright (c) 2025 Khabat A. Mohammed, Kharman A. Faraj https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-23
publication date: 2025-09-23
volume: 13
number: 2
starting page: 185
ending page: 192
doi: 10.14500/aro.12243

Wild Cherry (Prunus microcarpa) Ameliorates Azoxymethane-Induced Aberrant Crypt Foci in vivo
type: item
description: Colorectal cancer is the third diagnosed cancer across the globe despite modern therapeutic interventions. Prunus microcarpa has been consumed as a therapeutic tea for several human disorders; therefore, the study investigates the chemoprotective effects of Prunus microcarpa against azoxymethane-induced aberrant crypt foci (ACF) in rats. Fifty Sprague-Dawley rats were divided into five groups: a normal control group and untreated rats given saline; a reference group treated with 5-fluorouracil; and two groups treated with 500 mg/kg methanolic extracts of fruits and stems (MEPMF and MEPMS), separately, for two months. Additionally, all rats, except the normal controls, were injected with azoxymethane twice a week for two consecutive weeks and consumed sodium dextran sulfate-mixed water for seven days. The plant extracts exhibited significant resistance against AOM-induced colon carcinogenesis, as indicated by lower ACF formation, reduced glandular dysplasia, decreased hyperchromasia, and a higher organization of simple columnar epithelial cells compared to vehicle rats. Immunohistochemical results demonstrated increased modulatory effects of MEPMF on apoptosis mediators, evidenced by higher Bax and lower PCNA levels in colonic tissues compared to MEPMS. Prunus supplementation led to decreased oxidative stress and cellular infiltrations in colon tissues, as evidenced by increased endogenous antioxidants (SOD, CAT, and GPx) and reduced inflammatory mediators (TNF-α and IL-6) and lower levels of peroxidation byproducts (MDA), while preserving organ functions such as those of the liver and kidneys. This study presents safety margin and chemoprotective effects of P. microcarpa against AOM-induced colon cytotoxicity, evidencing a viable source for nutraceutical and biopharmaceutical formulation.
creator: Ahmed A. Abduljabbar
creator: Parween A. Ismail
rights: Copyright (c) 2025 Ahmed A. Abduljabbar, Parween A. Ismail https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-26
publication date: 2025-09-26
volume: 13
number: 2
starting page: 193
ending page: 201
doi: 10.14500/aro.12399

Dolomitization and Hypogenic Dissolution of the Eocene Avanah Formation, Iraqi Kurdistan
type: item
description: This study constrains the mechanism of extensive dolomitization and its impact on reservoir quality of the shallowwater marine ramp carbonates of the Avanah Formation (Eocene), Iraqi Kurdistan. The presence of shoal deposits, which semiisolate a lagoon water body from the open marine, suggests that dolomitization was by seepage reflux of brines. Nevertheless, the absence of eogenetic gypsum/anhydrite in the dolostones succession indicates that the dolomitizing fluids were mesohaline/penesaline brines formed during cycles of relative sea level (RSL) fall. Dolomitization resulted in the formation of abundant intercrystalline and moldic/vuggy pores. Restriction of dolomitization and related reservoir quality improvement to the lower part of the formation is attributed to an overall 3rd order fall in the RSL. Conversely, the lack of dolomitization in the upper part of the formation is attributed to deposition during 3rd order marine transgression, which prevented severe restriction and evaporation of the inner ramp and, consequently, inhibited the development of dolomitizing brines. It is suggested that hypogenic dissolution (karstification) by upward flow of aggressive fluids along faults and fractures during the Zagros Orogeny caused dissolution and considerable porosity and permeability improvement of the dolostones. A greater extent of dolostones dissolution in the flanks, which was accompanied by calcite cementation, compared to the crest, reflects the role of oil emplacement in the retardation of diagenetic reactions.
creator: Gazang H. Qadir
creator: Howri Mansurbeg
rights: Copyright (c) 2025 Gazang H. Qadir, Howri Mansurbeg https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-01
publication date: 2025-10-01
volume: 13
number: 2
starting page: 202
ending page: 214
doi: 10.14500/aro.12333

A Study of Large Language Models in Detecting Python Code Violations
type: item
description: Adhering 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.
creator: Hekar A. Mohammed Salih
creator: Qusay I. Sarhan
rights: Copyright (c) 2025 Hekar A. Mohammed Salih, Qusay I. Sarhan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-01
publication date: 2025-10-01
volume: 13
number: 2
starting page: 215
ending page: 225
doi: 10.14500/aro.12395

Uric Acid Dynamics in Young Women
type: item
description: A growing body of evidence implicates uric acid to play central roles in various disease complications. However, the delineation between causative and coincidental roles remains poorly elucidated. This study aimed to evaluating uric acid in young women considering variables such as disease types, body mass index, blood groups, radiation exposure, and to assess the relationship between uric acid levels and certain subclinical biomarkers. A cohort of 178 young women, between 18 and 39 years, (comprising 100 cases and 78 healthy controls) was included in this investigation. Diagnosed subjects with common diseases, renal, cardiovascular, blood, joints, diabetes, gastrointestinal diseases, were selected according to stringent criteria. Subjects were categorized based on disease types, blood groups, and exposure history to ionizing radiation including mammography and body or leg X-ray. Serum levels of uric acid and biochemical tests were analyzed by unpaired comparison tests and correlation coefficient tests. Subjects with hematological disorders exhibited lower uric acid concentrations when compared to healthy women. Uric acid concentrations in individuals with repeated exposure to X-ray radiation showed a high significant difference (p<0.01) compared to unexposed individuals. In Spearman correlation analyses, positive correlations (p < 0.001) are identified between uric acid and iron, calcium, prolactin, and body mass index. In healthy young women, uric acid levels fluctuated both within and beyond physiological ranges, independent of disease status, weight indexes, and blood types. Assessment of uric acid in young women, based on a history of radiation exposure, subclinical blood parameters might guide for better medical decision and treatment.
creator: Ahmed H. Ahmed
rights: Copyright (c) 2025 Ahmed H. Hasan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-11
publication date: 2025-10-11
volume: 13
number: 2
starting page: 226
ending page: 232
doi: 10.14500/aro.12380

Estimation of Atherogenic Index of Plasma, Prolactin, and Some Other Hormones in Polycystic Ovarian Syndrome in Lean Women
type: item
description: Polycystic 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.
creator: Sara S. Hamad
creator: Sardar N. Ahmed
rights: Copyright (c) 2025 Sara S. Hamad, Sardar N. Ahmed https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-07
publication date: 2025-11-07
volume: 13
number: 2
starting page: 256
ending page: 262
doi: 10.14500/aro.12406

Identification of OPN1LW Exon 3 Variants Impairing Red-Cone Function in Color Vision Deficiency
type: item
description: The most common form of inherited color blindness is red-green color vision deficiency (CVD), which is frequently caused by mutations in the X-linked OPN1LW gene. Red cone malfunction is linked to mutations in exon 3 of this gene. In this study, the Ishihara test was used to evaluate the color vision of 1500 Kurdish students, ages 13–18. Polymerase chain reaction amplification and Sanger sequencing of OPN1LW exon 3 were performed on 50 students who had been diagnosed with protanopia or protanomaly. Variants (nucleotide changes) were analyzed using Geneious Prime® software. Functional impact of variants was predicted using PolyPhen-2 and SIFT. The study found 30 different nucleotide variations, comprising 63.3% missense mutations, 23.3% silent mutations, and 13.3% frameshift mutations. The most common variants were found c.30G>A (p. Arg10Arg), c.106T>C (p. His35Pro), and c.161_162insG (p. Asp54Gly). SIFT found (57.8%) of variations as deleterious (scoring ≤0.05), but PolyPhen-2 assessed (63.1%) as potentially damaging (score >0.9). ABO blood type was unrelated to CVD risk, although consanguinity and family history were strongly linked to CVD risk. Our study revealed that people with red-green CVD have frequent and possibly harmful mutations in exon 3 of OPN1LW. These results may aid in the molecular characterization of CVD in the Kurdish population and could help develop future diagnostic and treatment approaches.
creator: Shaaban Z. Omar
creator: Karim J. Karim
rights: Copyright (c) 2025 Shaaban Z. Omar, Karim J. Karim https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
volume: 13
number: 2
starting page: 263
ending page: 277
doi: 10.14500/aro.12480

Spatio-Temporal Evaluation of Physicochemical Parameters, Heavy Metals, and Pesticides in the Little Zab River
type: item
description: Water quality is a fundamental determinant of human health, influencing the prevalence of waterborne diseases and the overall safety of potable water. Contaminated water sources expose populations to microbial pathogens and toxic chemical contaminants, presenting significant public health and environmental challenges. In this study, a comprehensive assessment of water quality was conducted on a segment of the Little Zab River, situated in the Kurdistan region of Iraq. Water samples were meticulously collected from five strategically selected sites along the river, spanning across all four seasons, to evaluate temporal variations in water quality. In situ measurements of critical physico-chemical parameters, including pH, electrical conductivity, total dissolved solids, salinity, dissolved oxygen, density, and turbidity, were performed to establish baseline water quality profiles. Concurrently, laboratory analyses were performed to quantify the concentrations of selected heavy metals (mercury, cadmium, arsenic, zinc, iron, lead, and copper) and pesticides (α-cypermethrin, acetamiprid, dichloro-diphenyl-trichloroethane, and p,p՛-DDD) using standardized methods. Descriptive statistical analyses, conducted using Excel and Statistical Package for Social Sciences, revealed significant spatial and seasonal fluctuations in both the physicochemical parameters and contaminant levels, with certain sites exhibiting concentrations that raise potential health and ecological concerns. The findings underscore the critical need for continuous monitoring of water used in agriculture and the implementation of targeted management strategies to mitigate contamination and protect public health in the region. This work contributes valuable insights into the interplay between natural processes and anthropogenic impacts on riverine water quality in arid and semi-arid environments.
creator: Mamoon Q. Salih
creator: Tara F. Tahir
creator: Wali M. Hamad
rights: Copyright (c) 2025 Mamoon Q. Salih, Tara F. Tahir, Wali M. Hamad https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-11
publication date: 2025-11-11
volume: 13
number: 2
starting page: 278
ending page: 292
doi: 10.14500/aro.12364

Influence of Structural Properties on Fusion Cross-Sections Using Proximity Potentials
type: item
description: In this study, an optimal nuclear proximity potential is used to get more accurate fusion cross-section predictions. For 111 colliding systems, we evaluate the predictive accuracy of several proximity potential models interfaced with Wong’s formula in reproducing the fusion cross-section experimental data. For the purpose of Chi-square minimization technique, Christensen and Winther 1976 potential is selected. The analysis examines fusion dynamics across a wide range of nuclear configurations (6 ≤ Zp (projectile atomic number)≤28, 6 ≤ Zt (target atomic number) ≤94, and 36 ≤ Zp Zt ≤ 1880). To increase accuracy and match experimental data, a Python code that calculates cross-sections for all proximity potentials is established using the Nelder–Mead algorithm. The extensive range of calculations facilitates a systematic study of the effects of structural factors, including magic numbers, shell structure, neutron excess, and pairing effect. The results reveal that shell effects can sometimes overcome neutron excess and produce unexpected fusion trends, as seen in the 28Si + 90Zr and 28Si + 94Zr. In other reactions, the shell effect eliminated the effect of the neutron excess, such as in the 16O + 62Ni versus 16O + 58Ni and 12C + 208Pb versus 12C + 204Pb reactions. Our findings also highlighted the important role of the projectile in the process of fusion. The titanium isotopes (46Ti, 50Ti) in particular fused more effectively with 12C than with 16O. Nickel isotopes show similar projectiledependent behavior.
creator: Lina S. Abdalmajid
creator: Adil M. Saeed
rights: Copyright (c) 2025 Adil M. Saeed, Lina S. Abdalmajid https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-12
publication date: 2025-11-12
volume: 13
number: 2
starting page: 293
ending page: 303
doi: 10.14500/aro.12265

Novel Design of a Lowpass-Bandpass Diplexer with an Exceptional Performance Using Microstrip Meandrous Loaded Lines for Microwave Systems
type: item
description: A new microstrip lowpass-bandpass diplexer with exceptional performance characteristics is designed and analyzed. It includes microstrip meandrous thin lines loaded by solid cells. The diplexer achieves 0.058 dB and 0.013 dB low insertion losses (ILs) and 26.4% wide fractional bandwidth (FBW). Operating frequencies are obtained using a perfect mathematical method, which are verified by the simulation results. Having two equations that accurately predict the operating frequencies allows us to optimize the diplexer's performance by adjusting key parameters such as line lengths and widths. This helps in achieving the desired operating frequencies with minimal trial and error. It has 0.01 λg2 very compact size and exhibits low group delays at both channels, which make it appropriate for a varied range of applications requiring high precision and efficiency. Overall, this microstrip diplexer design offers a versatile and reliable solution for demanding RF and microwave applications.
creator: Salah I. Yahya
creator: Mohammed Abdel Hafez
creator: Leila Nouri
creator: Farid Zubir
rights: Copyright (c) 2025 Salah I. Yahya, Mohammed A. Hafez, Leila Nouri , Farid Zubir https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 13
number: 2
starting page: 304
ending page: 313
doi: 10.14500/aro.12549

Modification of the Optical Properties of Polyvinyl Alcohol through Incorporating Cu2O Nanoparticles Prepared by Laser Ablation
type: item
description: A focused, high‐intensity pulsed laser ablated 99.9% - pure copper targets submerged in deionized water and in Polyvinyl alcohol (PVA) solution, respectively, was utilized to produce Cu2O nanoparticles (NPs). Nano-plasmonic cuprous oxide was incorporated using the nanosecond Nd: YAG pulsed laser ablation in liquids technique, which advances the physiochemical characteristics of Cu2O/PVA nanocomposite. Optical characterization was carried out for the induced Cu2O NPs and Cu2O/PVA nanocomposites with six different mass concentrations of Cu2O. The concentrations of the Cu2O NPs were 0.007, 0.017, 0.027, 0.04, 0.047, and 0.057 mg/mL in the PVA matrix. X-ray diffraction confirms that copper ions were reduced to form crystalline Cu2O NPs. Furthermore, DLS showed the presence of NP agglomeration, which revealed polydispersity of Cu2O NPs. The band gap of pristine PVA, determined from Tauc plots, was 5.00 eV. The optical band gap decreased progressively with increasing mass concentration of Cu2O NPs. This band-gap reduction is attributed to changes in the PVA electronic structure caused by incorporated Cu2O NPs. A distinctive feature of this work is the use of pulsed-laser ablation in liquid to generate plasmonic Cu2O NPs and incorporate them in situ into PVA at room temperature in a single step; NP concentration was precisely controlled by the number of laser pulses.
creator: Chro H. Othman
creator: Mohammed F. M. Sabri
creator: Simko O. Ramadan
rights: Copyright (c) 2025 Chro H. Othman, Mohammed F. M. Sabri, Simko O. Ramadan https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
volume: 13
number: 2
starting page: 314
ending page: 325
doi: 10.14500/aro.12222

Immunomodulatory Pathways of Some Biomarkers in Individuals with Renal Failure
type: item
description: This study investigates the correlation between renal failure and immunoinflammatory marker pathways. Specimens were obtained from the Hemodialysis Center located in the cities of Ranya and QaladezeSulaymaniyah, KRG, Iraq. The investigation included a well-defined cohort of 120 individuals (60 patients and 60 control participants), comprising both sexes, during the period from October to December 2024. Participants ranged in age from 40 to 60 years. The concentrations of immunoglobulins (IgA, IgM, and IgG) were measured using the Cobas 6000 analyzer, while IgE levels were assessed with the Cobas e 411 analyzer. The enzyme-linked immunosorbent assay was utilized for detecting the variations of cytokines (Tumor necrosis factor-alpha, interleukin (IL)-6, and IL-10) and antinuclear antibody (ANA). Erythrocyte sedimentation rate (ESR) was determined using the automated ESR analyzer. Individuals diagnosed with end-stage renal disease (ESRD) exhibited significant immunological alterations, marked by a significant decrease in IgA, IgM, and IgG levels and accompanied by a substantial elevation in IgE levels. The current investigation revealed a markedly elevated ESR, tumor necrosis factor- alpha (TNF-α), IL-6, and IL-10 in reference to the healthy subjects. ANA titers exhibited a slight increase within the patient population. This immunological dysregulation is further associated with elevated levels of TNF-α, IL-6, and IL-10, indicating a persistent systemic inflammatory state. The synchronous augmentation of ESR and TNF-α and IL-6 confirms their potential utility as prognostic biomarkers for chronic inflammation in ESRD patients. Observation of elevated inflammatory markers in females indicates a potential gender-specific variation in immune response pathways within this clinical population.
creator: Ahmed F. Shallal
rights: Copyright (c) 2025 Ahmed F. Shallal https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-06
publication date: 2025-12-06
volume: 13
number: 2
starting page: 326
ending page: 333
doi: 10.14500/aro.12474

Assessment of Corrosion Inhibition Efficiency of Some Amino Acids on Stainless Steel in Ethaline
type: item
description: This study evaluates simple amino acids as green corrosion inhibitors for AISI 302 stainless steel in Ethaline, a chloride-rich deep eutectic solvent (DES). General and localized corrosion behaviors were assessed using Tafel analysis and cyclic potentiodynamic polarization (CPP) in blank Ethaline and with 0.05 M, 0.075 M, and 0.1 M glycine, alanine, and leucine.​ Results show complex interactions between amino acid structure, concentration, and inhibition. The high viscosity of Ethaline shifts anodic control from charge transfer to mass transfer at higher potentials. All amino acids shift corrosion potential (Ecorr) nobly, with 0.05 M glycine giving the largest increase (~0.26 V) for general corrosion resistance, though it yields the narrowest passivation range (0.52 V), increasing pitting susceptibility. Alanine and leucine, with bulkier side chains, enhance localized corrosion resistance via steric hindrance to Cl⁻ ions.​ Low concentrations (0.05 M) optimize protection by lowering passivation current (ipass) and forming stable films, but higher levels (>0.05 M for glycine/leucine) reverse this, accelerating corrosion via acidity or complexation. A 0.05 M glycine/leucine binary mixture produces negative CPP hysteresis, indicating strong repassivation and self-healing.​ These findings reveal trade-offs between general and localized protection, emphasizing precise concentration control. The study advances tailored, synergistic amino acid inhibitors as sustainable, self-healing systems for DES environments.
creator: Essa S. Ahmed
creator: Hassan H. Abdallah
rights: Copyright (c) 2025 Essa S. Ahmed, Hassan H. Abdallah https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-07
publication date: 2025-12-07
volume: 13
number: 2
starting page: 334
ending page: 340
doi: 10.14500/aro.12051

Using Structural Data to Indicate the Folding Type of Anticlines Examples from the Kurdistan Region, Iraq
type: item
description: Using seismic data is the most relevant and sound method to indicate the folding type in anticlines. However, when seismic data are not available or not allowed for use, then structural data are recommended to indicate the folding type of any anticline. Such usage is known and considered worldwide with excellent results. We have adopted a sound and published opinion of using structural data to indicate the folding type, and applied the method on three anticlines, namely, Qara Boutaq, Korek, and Mateen. We have selected the three anticlines from three different tectonic zones in the Iraqi Kurdistan Region, which is located in the northern and northeastern parts of Iraq, and forms the northeastern part of the Arabian Plate. The plate has been colliding with the Eurasian Plate since the Late Cretaceous and is still in collision; accordingly, all the existing anticlines are developed. We used satellite images to measure the required structural parameters, including aspect ratio, fold symmetry index, and Mountain Front Sinuosity Index in these anticlines. We also measured the dip amounts along the three anticlines from satellite images, where there are no dip measurements in the geological maps, to indicate the geometrical type of the three anticlines. Part of the interpreted data was checked and confirmed in the field, wherever it was accessible. Accordingly, we indicated the folding type to be Detachment Fold for the Qara Boutaq and Korek anticlines and Fault-bent Fold for the Mateen anticline.
creator: Varoujan K. Sissakian
creator: Lanja H. Abdullah
rights: Copyright (c) 2025 Varoujan K. Sissakian, Lanja Abdullah https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-08
publication date: 2025-12-08
volume: 13
number: 2
starting page: 341
ending page: 348
doi: 10.14500/aro.12382

An Ensemble-based Model for Sentiment Analysis of Kurdish Tweets
type: item
description: Thousands of comments are generated daily on social media in the Kurdistan Region. Sentiment analysis (SA) of these comments is valuable for organizations. The Kurdish language has three main dialects: Sorani (Central), Northern, and Southern. This study focuses on Sorani SA, where existing methods have limited accuracy. The proposed ensemble combines diverse models to improve sentiment classification. Preprocessing and word embedding using Roberta is the first phase of the method. The second phase consists of four proposed models, namely K-nearest neighbor, support vector machine, multilayer perceptron long short-term memory (LSTM), and bidirectional-LSTM (Bi-LSTM), which are used as classifiers. Finally, the ensemble weighted averaging technique is utilized to generate the final classification. To evaluate the performance of the proposed model, a dataset including 24211 unbalanced Soran tweets is first used, and after balancing, the dataset is used. The Bi-LSTM model attained an accuracy of 89.87% on the balanced dataset, and the proposed ensemble method increased the accuracy to 91.76%, which is better than the established state-of-the-art methods of Kurdish SA.
creator: Sabat S. Muhamad
creator: Abdulhady A. Abdullah
creator: Hakem Beitollahi
creator: Shamal A. Abdullah
creator: Rezhin S. Shahab
creator: Ashna D. Zrar
rights: Copyright (c) 2025 Sabat S. Muhamad, Abdulhady A. Abdullah, Hakem Beitollahi, Shamal A. Abdullah, Rezhin S. Shahab, Ashna D. Zrar https://creativecommons.org/licenses/by-nc-sa/4.0
license: https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-11
publication date: 2025-12-11
volume: 13
number: 2
starting page: 349
ending page: 358
doi: 10.14500/aro.12255