ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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.
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The high-speed development of Fifth Generation technologies announces a new era for the internet of things (IoT), distinguished by high-rate connectivity, speed, and low latency. However, this advancement also opens doors to major security challenges and expands the attack surface. Existing general IoT surveys do not systematically analyze Fifth Generation-enabled IoT concerns, which creates a clear need for a focused synthesis of machine learning and deep learning (DL) defenses tailored to Fifth Generation-IoT constraints and threat models. To address this gap, this survey conducts a preferred reporting items for systematic reviews and meta-analyses-guided analysis of recent studies published in the past 5 years, extracting methodologies, results, datasets, metrics, tools, and reported limitations to answer explicit research questions about which approaches work, under which conditions, and with what deployment implications for Fifth Generation IoT threat detection and mitigation. The results show that DL families and hybrid deep models dominate intrusion, anomaly, and malicious traffic detection, while research overemphasizes denial-of-service attacks relative to Replay, Ransomware, Sybil, Man-in-the-Middle, and Phishing attacks. The recommendations, which come from comparative evidence across datasets, attack categories, and model performance limitations, emphasize the need for more diverse and realistic Fifth Generation IoT datasets as well as for understudied learning paradigms, such as continual learning, federated learning, meta-learning, and self-supervised learning. These insights highlight clear research directions toward adaptive, privacy-preserving, and generalizable intrusion detection in Fifth Generation-IoT systems.
Copyright (c) 2026 Umed H. Jader, Reben Kurda, Sara R. Muhamad https://creativecommons.org/licenses/by-nc-sa/4.0
Distributed denial of service (DDoS) attacks are a significant danger to network security, with SYN flood assaults being particularly known for exploiting the transmission control protocol (TCP) handshake to deplete server resources. This review paper analyzes the current research on classifying DDoS attacks using machine learning (ML) approaches, with a focus on SYN f lood scenarios. Traditional algorithms such as XGBoost, Random Forest, and k-Nearest Neighbors are examined alongside modern deep learning methods such as convolutional neural networks and long short-term memory networks. Deep learning, noted for its capacity to automatically learn complex properties from data, is particularly effective in dynamic contexts like the internet of things. The review analyzes the usefulness of various strategies, obstacles in feature engineering and model training, and their implications for real-time detection. This study presents a comprehensive overview of the accomplishments in employing ML and deep learning for TCP SYN flood attack classification and exposes gaps in the field that indicate options for further research.
Copyright (c) 2026 Soran S. Hamad ; Kayhan Z. Ghafoor https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Review of IoT Attack Detection
The lack of security measures in Internet of Things (IoT) systems has made these tiny devices vulnerable to increasingly advanced and evolving cyber-attacks. Attack detection and prevention are one of the most promising approaches to mitigating these attacks. However, these techniques require more computing, memory, and energy than typical IoT devices can provide. In addition, the IoT network is distributed, heterogeneous, and dynamic. These limitations motivate this review to examine the effective use of machine learning and deep learning in detecting attacks on IoT systems. Despite the presence of prior studies on reviewing these techniques, there is still a gap in analyzing attack vectors and assessing the effectiveness of current detection techniques for IoT networks and environments, especially in terms of lightweight and real-time evaluation. In this work, a multidimensional taxonomy of IoT attacks and existing detection techniques is given. The included studies were critically analyzed to evaluate and assess their effectiveness, considering performance metrics, IoT system architecture, datasets, and deployment strategies. The core methodologies of the analyzed studies were examined to guide academia and industry in improving detection techniques. Results showed that most of the proposed techniques in the literature did not address IoT-specific requirements. However, techniques featuring lightweight, real-time, federated, and scalable solutions have been proposed, yet their practical effectiveness remains unvalidated. This review addresses key research gaps and future challenges, emphasizing the need for resource-efficient, adaptable detection methods that align with IoT constraints.
Copyright (c) 2026 Shilan S. Hameed https://creativecommons.org/licenses/by-nc-sa/4.0
In response to the demographic bias commonly observed in facial beauty prediction (FBP) models due to the underrepresentation of certain ethnic groups, KurdFace-1000 is introduced as a novel and balanced dataset developed as a field of research community. This dataset comprises 1000 color facial images of individuals from the Kurdistan Region of Iraq, evenly distributed across gender (500 male, 500 female) and facial expression (500 smiling, 500 non-smiling), and includes both frontal and profile views. Within the results of males, 200 are smiled and 300 are non-smiled and for the results of females, 300 are smiled and 200 are non-smiled. Moreover, each image is annotated with three key attributes: A beauty score on a [1–5] scale rated by five independent human raters, binary gender, and smile expression. KurdFace-1000 is the first dataset specifically designed to represent Kurdish facial features in FBP tasks, aiming to reduce ethnic bias and improve model performance for underrepresented populations. With a balanced structure and diverse annotations, the dataset supports various computational paradigms, including classification and regression, and serves as a critical step toward building culturally aware and inclusive deep learning models in FBP.
Copyright (c) 2026 Ali H. Ibrahem, Adnan M. Abdelazeez https://creativecommons.org/licenses/by-nc-sa/4.0
Development and Performance Assessment of a Novel Integrated MSBR for Sustainable Domestic Wastewater Treatment
This study presents a novel integrated modification of the Sequencing Batch Reactor (MSBR), combining the operational flexibility of conventional SBRs with the biomass retention and stability of a Moving Bed Biofilm Reactor (MBBR) through the incorporation of an upstream equalisation tank containing floating media and diffusers. The performance of the MSBR was evaluated against a conventional SBR and an MBBR under identical operating conditions. Over a 12-month monitoring period, influent and effluent samples were analysed for 25 physicochemical parameters representing organic load, nutrient dynamics, physical water quality, and mineral or trace element composition. Statistical analyses were conducted using IBM SPSS Statistics version 25. The MSBR consistently achieved the highest treatment performance. COD and BOD removal efficiencies reached 92.2% and 92.4%, compared with 88.5% and 88.7% in the MBBR and 82.6% and 82.9% in the SBR. Total nitrogen reduction was also higher in the MSBR (91.1%) than in the MBBR (79.9%) and SBR (85.7%), while total phosphorus removal reached 96%, outperforming both the MBBR (79.4%) and the SBR (89.5%). Sulfate removal remained moderate across all systems (MBBR 65.8%, MSBR 64.9%, SBR 57.1%). Although the SBR showed slightly higher oil and grease removal (83.4%), this marginal difference is outweighed by the MSBR’s superior nutrient and organic matter removal. Seasonal and monthly analyses confirmed the operational robustness of the MSBR. Overall, the results demonstrate that the integrated MSBR is a reliable and superior alternative for sustainable domestic wastewater treatment.
Copyright (c) 2026 Hanaa A. Muhammad, Bakhtyar A. Othman, Galawezh B. Bapeer https://creativecommons.org/licenses/by-nc-sa/4.0
Analyzing Thermal Efficiency with Pin Fins, Perforated Bases, SolidWorks 2023 Transient Analysis
A group of fifteen central processing unit (CPU) heat sinks of circular, hexagonal, and rectangular shapes with different f in designs and perforation directions was created and analyzed using SolidWorks 2023. In boundary conditions, a heat flux of 65 W was applied, while the surrounding air was at 25°C, and a heat transfer coefficient of 6 W/m2·K was considered. Perforation caused a weight decrease of up to 67.4% in the heat sinks, while perforation orientations had a greater effect on heat dissipation than perforation size. In heat sinks with vertically perforated pins and base R5, the lowest maximum temperature of 77.5°C, the lowest weight of 412.56 g, and the best heat dissipation occurred.
Copyright (c) 2026 Serwan S. Talabani https://creativecommons.org/licenses/by-nc-sa/4.0
Implementation of Fingerprint Biometrics for Secure Contactless Banking Card Transactions
A contactless card is an easy and straightforward way to make a purchase that takes only a few seconds without requiring a Personal Identification Number (PIN) or signature. However, not requiring a PIN or signature on a contactless bank card makes it vulnerable to fraud attacks when losing or stealing the card. This article delivers a new model which is developed for securing a bank contactless card with fingerprint authentication. The suggested model is the creation of a new fingerprinting algorithm that combines with the virtual contactless card. The fingerprint recognition algorithm employs image processing methods to enhance and extract features in order to compare fingerprint impression images. The performance of the proposed model is evaluated based on two metrics: false acceptance rate and false rejection rate. There are five scenarios to test and evaluate the proposed model. The findings establish that the developed system enhances the process of embedding biometrics (fingerprints) to non-contact smart card and a user-friendly experience.
Copyright (c) 2026 Soleen J. Ibrahim, Shaheen A. Abdulkareem, Ahmad B. Al-Khalil https://creativecommons.org/licenses/by-nc-sa/4.0
Pediatric Respiratory Tract Infections and Circulating Influenza A Virus Subtypes
Influenza and other viral respiratory infections are among the top three leading causes of morbidity and mortality in young children. This study aimed to investigate demographic and clinical characteristics of acute respiratory tract infection (ARTI) and to determine the incidence of influenza A virus subtypes among children in winter in Erbil, Iraq. A cross-sectional study was implemented to investigate the criteria of ARTI in pediatric patients aged ≤5 years who visited Raparin Teaching Hospital for Children from December 1, 2024, to February 28, 2025. A detailed history was collected, and multiplex reverse transcription quantitative polymerase chain reaction was employed for influenza A detection and subtyping. Out of 200 patients, the virus was detected in 14%, with H1N1pdm09 being the predominant subtype (10.5%). Most cases (55%) were hospitalized with severe acute respiratory infection, with ages ≤6 months showing higher susceptibility. Clinically, bronchiolitis was the main clinical diagnosis (66.5%), with hypoxia observed in 36% of cases. Infection among family members was a significant risk factor for pediatric ARTI (p = 0.045). Influenza A showed no significant correlation with hospitalization or age (p = 0.512 and p = 0.987, respectively) but demonstrated strong seasonal variation, peaking in December (p < 0.001). In conclusion, influenza A virus contributed notably, though not predominantly, to pediatric ARTI in Erbil City, with H1N1pdm09 as the predominant subtype and a peak in December. Most cases involved lower respiratory tract disease and hypoxia. Younger age and household infection increase susceptibility, highlighting the need for routine surveillance and preventive measures.
Copyright (c) 2026 Sazan M. Abdulaziz https://creativecommons.org/licenses/by-nc-sa/4.0
Experimental Assessment and Prediction of Physical Properties of Nanosilica-Modified Asphalt Cement
Modifying the asphalt binder is one technique for improving asphalt pavement performance. When nanoparticles are utilized to modify asphalt binders, they exhibit unique and significant properties. Without a doubt, silica is the most commonly added element as nanoparticles to asphalts. The main objective of this study was to investigate the effect of mixing speed used to produce nanosilica (NS)-modified-asphalt cement on conventional physical properties of asphalt cement and prepare a model to predict the physical properties of NS-modified asphalt cement. For this purpose, four different nanosilica contents (1.5%, 3%, 4.5%, and 6% of the asphalt cement weight) were utilized. Each content was mixed at a temperature of 160°C using a high shear mixer set to four different speeds (1,000 rpm, 2,000 rpm, 3,000 rpm, and 4,000 rpm) for 30 min. Conventional asphalt cement tests, including penetration, softening point, rotational viscosity, and ductility tests, are conducted to evaluate the nanosilica-modified asphalt cement properties. The results showed that higher NS contents improve the physical properties of modified asphalt cement, but the improvement is more noticeable when mixing at a faster speed, as this lowers penetration and rotational viscosity and raises the softening point. By utilizing the Minitab statistical program, the physical properties prediction models of the NSmodified asphalt showed a direct correlation between input data (NS content, NS average size, mixing temperature, mixing rotational speed, mixing time, and neat asphalt properties) and predictive models, demonstrated by a high R2 value and acceptable standard deviation values.
Copyright (c) 2026 Sheelan A. Ahmed, Hirsh M. Majid https://creativecommons.org/licenses/by-nc-sa/4.0
Evaluating Large Language Models for Arduino Code Generation
Large language models (LLMs), also known as generative AI, have transformed code generation by translating natural language prompts into executable code. Yet, their capabilities in generating code for resource-constrained devices such as Arduino, which are used in the Internet of Things and embedded systems, remained underexplored. This study evaluates six state-of-the-art LLMs for generating correct, efficient, and high-quality Arduino code. The evaluation was performed across five dimensions, namely functional correctness, runtime efficiency, memory usage, code quality, similarity to human-written code, and multi-round error correction. The results reveal that ChatGPT-4o achieves the highest zero-shot functional correctness and aligns closely with human code in readability and similarity. On the other hand, Gemini 2.0 Flash generates faster-executing code but at the cost of higher code complexity and lower similarity. DeepSeek-V3 balances correctness with superior flash memory optimization, whereas Claude 3.5 Sonnet struggles with prompt adherence. Finally, multi-round error correction improves correctness across all six models. Overall, the f indings underscore that none of the evaluated LLMs consistently outperforms all evaluation criteria. Hence, model choice must align with project priorities; as shown, ChatGPT-4o excels in functional correctness, whereas Gemini 2.0 excels in execution time, and DeepSeek-V3 in memory efficiency. This study provides a systematic evaluation of code generated with LLMs for Arduino, which, to the best of our knowledge, has not been previously studied across multiple models and performance metrics, thereby establishing a foundation for future research and contributing to enhancing the trustworthiness and effectiveness of LLM-generated code.
Copyright (c) 2025 Sardar K. Jabrw, Qusay I. Sarhan https://creativecommons.org/licenses/by-nc-sa/4.0
The significance of creating models that can produce precise textual descriptions of photographs has become apparent, particularly in specialized domains such as fashion. Arabic suffers from a severe shortage of publicly available resources, particularly fashion picture databases, in contrast to the wealth of databases and studies about the English language. This restricts the creation of Arabic language models and impedes scholarly research in this area. By creating a hybrid model for automatically producing Arabic descriptions of fashion photos, our study seeks to close this gap. Based on the EfficientNet-B4 architecture, this model incorporates an attention mechanism to extract visual features and, for the first time in this field, links it to an xLSTM module for text creation. This study produced a new dataset with Arabic captions called AraFashion; the Arabic descriptions were translated into English through Google Translate. Using real Arabic data improves the model’s accuracy and realism, as seen by the model’s top BLEU-1 score of 0.7335 for Arabic descriptions. This study suggests growing Arabic databases in the fashion industry and highlights the need to support the Arabic language in AI technology.
Copyright (c) 2026 Shams A. Ahmed, Ahmed T. Abdulameer https://creativecommons.org/licenses/by-nc-sa/4.0
Improving Breast Cancer Classification with Adaptive Synthetic Sampling, Feature Selection, and Hyperparameter Optimization
Breast cancer is a major global health concern, highlighting the need for accurate and efficient diagnostic solutions rather than persistent issues with detection accuracy. This study presents an enhanced machine learning framework to improve breast cancer classification by addressing key limitations: Class imbalance, irrelevant features, and suboptimal hyperparameters. Adaptive synthetic sampling (ADASYN) was used to balance class distribution and various feature selection techniques. Univariate Selection and recursive feature elimination improved feature relevance, and arctic puffin optimization (APO) was applied for hyperparameter tuning. Multiple classifiers were evaluated using the Wisconsin Diagnostic Breast Cancer dataset. The random forest (RF) with ADASYN approach, optimized using APO, achieved outstanding results – 99.53% accuracy, 100% precision, 99.07% recall, and 99.53% F1-score – with only one misclassification out of 569 samples. This framework, while not modifying ADASYN or RF algorithms themselves, significantly enhances diagnostic performance and serves as a robust foundation for clinical decision support systems.
Copyright (c) 2026 Hayder N. Jasim, Wesam M. Jasim, Mohammed S. Ibrahim https://creativecommons.org/licenses/by-nc-sa/4.0
Assessment of Interleukin-31 and Calprotectin in Immunocompromised Patients with Epstein-Barr Virus Infection
Epstein-Barr virus (EBV) is among the most concerning herpesviruses, despite its typically asymptomatic or mildly self-limiting infections. The virus’s carcinogenic features and its ability to modulate the immune system are critical. That is why more research is needed on the hidden aspects of the virus’s life cycle, especially its oncogenic role and post-transplant complications. The study aims to harness EBV’s unique characteristics to gain new insights into its pathogenesis. This study was designed to investigate immunological and inflammatory biomarkers among three immunocompromised groups compared with an immunocompetent group in Sulaimani City (n = 81), including identification of EBV seropositivity in association with interleukin (IL) 31, calprotectin, lactate dehydrogenase, total leukocytes, granulocytes, lymphocytes, and platelets. EBV viral capsid antigen (VCA) immunoglobulin (Ig)M seropositivity was 29%, 21%, 15%, and 0%, in the immunocompetent, renal transplant, bone marrow transplant (BMT), and hematological malignancy groups, respectively. In addition, VCA IgM was inversely correlated with age (r = −0.353, p = 0.001). Interestingly, a significant association between the EBV VCA IgM index and IL-31, and, consequently, calprotectin levels, was observed. Regarding hematological parameters, the study found no significant association with EBV seropositivity. In conclusion, EBV latent Ig was less commonly recorded in the hematology and BMT groups than in the kidney transplant and immunocompetent groups. The findings suggest a possible association between IL-31 and calprotectin levels with EBV seropositivity; however, additional mechanistic studies are required to determine causality or oncogenic relevance.
Copyright (c) 2026 Awat R. Rahim-Majid, Muhammed Babakir-Mina https://creativecommons.org/licenses/by-nc-sa/4.0
Immunological Impact of Calprotectin and Interleukin-34 in Immunocompromised Patients with Chronic Cytomegalovirus Infection
Cytomegalovirus (CMV) causes the most prevalent and severe opportunistic infection in immunocompromised patients following solid organ or hematopoietic stem cell transplantation, with the highest morbidity and mortality rates among herpesviruses. The study objective was to compare and determine the CMV chronic infection and related hematological and immunological markers in immunodeficient and immunocompetent participants. A prospective case–control study among 85 participants was designed to measure CMV-immunoglobulin G (IgG) and to evaluate interleukin-34 and serum calprotectin as biomarkers; total leukocyte, granulocyte, lymphocyte, and platelet counts were also measured following blood collection. A high CMV IgG positivity was observed across all groups in this investigation, indicating widespread chronic infection. CMV IgG, interleukin-34 (IL-34), and calprotectin levels did not differ significantly between immunocompetent and immunocompromised individuals. There was no significant association between CMV IgG and IL-34 or serum calprotectin. Furthermore, IL-34 showed a significantly higher mean in males compared to females (p = 0.002). An exploratory observation was that IL-34 had a moderate positive correlation with serum calprotectin (ρ = 0.609, 95% confidence interval: 0.450–0.731, p < 0.001) across all study participants. The study findings call for more research to elucidate the clinical roles of calprotectin and IL-34 in immunocompromised patients.
Copyright (c) 2026 Honya Hamasalim, Muhammed Babakir-Mina https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Wastewater Aeration Time in Decentralized Sequencing Batch Reactors in Duhok City, Iraq
Aeration costs represent a significant portion of operational expenses (OPEX) in wastewater treatment facilities. Optimizing aeration time can enhance plant sustainability and also contribute to global CO2 reduction goals. However, research on aeration time optimization in full-scale decentralized Sequencing Batch Reactors (SBRs) remains limited. This study presents the first systematic application of BioWin to evaluate the impact of aeration time optimization on energy consumption, operational costs, and CO2 eemissions in full-scale decentralized SBR plants treating domestic wastewater. Operational, laboratory, and field data were used to develop influent profiles and simulation models. BioWin’s simulation based results indicated that aeration in the equalization (EQ) basin is not required to meet Iraqi effluent standards and that bioreactor aeration times can be optimized without compromising treatment performance, indicating that existing SBR systems are over-aerated. Optimized aeration time resulted in 58.70% reductions in energy use, operating costs, and CO2 emissions, resulting in annual OPEX cost savings of 234,049,308 IQD and demonstrating that aeration time optimization is a practical and cost-effective approach for improving the sustainability of decentralized SBR plants.
Copyright (c) 2026 Diler N. Hasan, Nashwan Sh. Mizzouri https://creativecommons.org/licenses/by-nc-sa/4.0
Prevalence and Molecular Diagnosis of Cryptosporidium ssp. and Giardia lamblia in Fresh Vegetables from Kurdistan Region/Iraq
Giardia lamblia and Cryptosporidium species are common intestinal parasites that contaminate fresh vegetables and create significant public health problems. This study investigates the prevalence and genetic diversity of six fresh vegetables sourced from agricultural fields and markets in the KRG-Iraq. The vegetable samples (n = 210) are obtained from local farms and markets during summer and autumn, 2024, including garden cress (Lepidium sativum), leek (Allium ampeloprasum var. porrum), lettuce (Lactuca sativa), parsley (Petroselinum crispum), spinach (Spinacia oleracea), and rocket (Eruca vesicaria). Samples are examined microscopically, stained with acid-fast stain, and subjected to molecular identification through polymerase chain reaction, followed by nucleotide sequencing. Molecular analysis revealed that 75 samples (35.7%) tested positive for Cryptosporidium, whereas 16 samples (7.6%) tested positive for Giardia. Cryptosporidium exhibited a higher prevalence. Garden cress had the highest level of contamination, with 68.6% of its samples testing positive for both parasites. Lettuce and leek exhibited the lowest percentage, approximately 11%. Sequencing identified the isolates as Cryptosporidium parvum and G. lamblia. A significant correlation (p < 0.05) existed between the kind of vegetable and the incidence of Cryptosporidium cases; however, no such correlation was observed between the type of vegetable and the incidence of Giardia cases. Overall, this study provides important information on the frequency of Cryptosporidium spp. and G. lamblia detection in six common vegetables consumed in Kurdistan, Iraq. Moreover, it shows how important molecular identification is for correctly identifying species and coming up with good ways to stop the spread of these diseases through food.
Copyright (c) 2026 Renas G. Abdullah, Abdullah A. Hama https://creativecommons.org/licenses/by-nc-sa/4.0
Clinical and Immunological Insights into Oral Candidiasis
Oral candidiasis (OC) is a widespread opportunistic fungal disease primarily caused by Candida albicans, with non-albicans Candida species progressively recognized as new pathogens. Previous studies have typically focused on a single microbial feature, such as the frequency of Candida species or biofilm formation, or on host immune responses, including cytokine activity, but have rarely investigated both simultaneously. These gaps have limited understanding of how fungal virulence factors interact with host immunity and have hindered the identification of reliable disease biomarkers. The present investigation examined the prevalence, species distribution, biofilm activity, and host immune responses in OC participants in Kurdistan, Iraq. A total of 154 suspected cases were directly cultured on Sabouraud Dextrose Agar and HiCrome™ Candida Differential agar and analyzed with the VITEK 2 system. Among them, 61 participants (39.6%) tested positive for OC, with C. albicans as the predominant isolate (59.4%), followed by Candida kefyr, Candida dubliniensis, Candida parapsilosis, Candida tropicalis, and Candida krusei. More than half of the isolates produced biofilm, and C. tropicalis exhibited the strongest biofilm-forming capacity. Immunological profiling revealed significantly higher neutrophil counts and elevated serum interleukin (IL)-10 and IL-17 in OC-positive participants. Diabetes mellitus appeared as the most common comorbidity. By integrating Candida species characterization with host immune profiling, this research provides new insights into the interaction between host defense mechanisms and fungal pathogenicity. In contrast to earlier work, the current analysis directly related species-specific biofilm capacity with neutrophil and cytokine dynamics, establishing IL-17 as a potential biomarker of OC activity.
Copyright (c) 2026 Sawan M. Mirkhan, Taha J. Zrary https://creativecommons.org/licenses/by-nc-sa/4.0
Molecular Evaluation of Interleukin-8, Interleukin-27, and Vascular Endothelial Growth Factor Expression in Prostate Cancer Patients
Globally, prostate cancer (PCa) is becoming more prevalent and lethal. The progression of PCa is linked to both inflammation and angiogenesis. Pro-inflammatory cytokines, interleukin (IL)-8) and vascular endothelial growth factor (VEGF), promote inflammation, angiogenesis, and PCa progression. On the other hand, IL-27 (IL-27) has antitumor effects, modulates immune system activity, and thus serves as a suppressor of tumor growth. In the Kurdistan Region of Iraq, there is limited evidence linking these cytokines and their gene polymorphisms to PCa. This case–control study included 50 PCa patients and 30 age-matched healthy controls. Serum levels of these three cytokines were evaluated by enzyme-linked immunosorbent assay, whereas PCR and Sanger sequencing were used to find polymorphisms in IL-8 (rs4073, rs2227306), IL-27 (rs153109), and VEGF (rs2010963) in formalin-fixed paraffinembedded tissue DNA. GeneMANIA was used to assess gene-gene interaction networks. Statistical analyses were performed using Mann–Whitney U tests. Patients with PCa showed elevated IL-8 and VEGF levels and reduced IL-27 levels compared to controls (p < 0.05). Multiple single-nucleotide polymorphisms were found in all target genes, several of which were new to the GenBank. Gene-network analysis revealed that these three cytokines are involved in shared inflammatory, immunomodulatory, and angiogenic pathways. This study shows that altered serum levels and gene polymorphisms of these three cytokines may be biomarkers for PCa diagnosis and progression. It underlines the molecular interplay between inflammatory and angiogenic mediators and supports further cytokine-based diagnostic and therapeutic research.
Copyright (c) 2026 Jutyar I. Saber, Hiwa R. Fatah, Fikry A. Qadir https://creativecommons.org/licenses/by-nc-sa/4.0
Biofabrication and Characterization of Zinc Oxide Nanoparticles Using Staphylococcus aureus
Due to their large surface area and catalytic properties, zinc oxide nanoparticles (ZnO-NPs) are highly effective in biological applications. In this study, ZnO-NPs were biofabricated using the American Type Culture Collection Staphylococcus aureus (ATCC 25923) and characterized by double-beam ultraviolet-visible spectroscopy, with a characteristic absorption peak at 351 nm, verifying the synthesis of ZnO-NPs. Proteins and carboxyl and hydroxyl groups that function as reducing and stabilizing agents were found on the surface of biosynthesized ZnO-NPs, as revealed by Fourier transform infrared analysis. The hexagonal structure was validated by X-ray diffraction analysis. Spherical shape confirmed by Field Emission Scanning Electron Microscopy, and the main elements detected by energy-dispersive spectroscopy were Zinc 79.91% and oxygen 18.33%. Transmission electron microscopy investigation revealed that the ZnO-NPs produced were predominantly quasi-spherical to irregular shape, with diameters in the nanoscale range (approximately 40–60 nm). The results indicated that the diameter of inhibitory zones against methicillin resistant S. aureus (MRSA) using varying concentrations of 50, 25, 12.5, 6.25, and 3.125 μg/mL of ZnO-NPs in an agar well diffusion test ranged from 7 to 24 mm, and the cytotoxicity of ZnO-NPs was assessed by MTT assay. Human embryonic kidney 293 cells were used. Our research demonstrates that biofabricated ZnO-NPs using standard S. aureus exhibit successful characterization and strong anti-MRSA activity, which may represent a promising path for the development of innovative antimicrobial agents, particularly for treating MRSA as multidrug-resistant isolates.
Copyright (c) 2026 Venus D. Najeeb, Fattma A. Ali, Sameira S. Swilaiman https://creativecommons.org/licenses/by-nc-sa/4.0
A Design of Power Amplifier using Enhanced Simple Real Frequency Technique
In this paper, a novel discrete wide-band microstrip power amplifier (PA) is designed and fabricated based on the enhanced simple real frequency technique (ESRFT) to achieve a higher power added efficiency (PAE) over a wide frequency range compared to the conventional simple real frequency technique (SRFT). To compare the ESRFT with the SRFT, a basic PA using the SRFT is designed additionally. The ESRFT applies the feedback effects that play an important role in the PA design process, especially for high-frequency applications. Unlike the SRFT, the ESRFT considers both the back-end and the front-end impedance of the active device, while it is loaded by the designed input matching network (IMN) and the output matching network (OMN), respectively. The parasitic feedback elements of the transistor disturb the designed IMN and OMN. Therefore, an iterative method can converge to a better result. The matching networks (MNs) are modeled as a single matching problem, formulated and calculated in the Richard domain. The MNs are constructed by cascading microstrip unit elements (UEs) to obtain a wide-band simple structure. The characteristic impedances of the UEs are derived using the Richard extraction process. The fabricated PA shows good agreement between the simulation and measurement results. It has a wide-band gain of up to 13 dB from 0.8 GHz to 1.6 GHz, with a maximum PAE of about 70% that has been improved compared to the designed basic PA. Furthermore, the third harmonic is suppressed up to 25 dBc.
Copyright (c) 2026 Alireza Golestanifar, Gholamreza Karimi, Ali Lalbakhsh https://creativecommons.org/licenses/by-nc-sa/4.0
Modeling the Influence of Nitrogen Concentration on Optical Gain Performance in Gallium Nitride Arsenide/Gallium Arsenide Quantum Wells Vertical-cavity Surface-emitting Lasers
This study investigates the influence of nitrogen (N) concentration on the optical and gain characteristics of Gallium Arsenide (GaAs)/gallium arsenide (GaAs) quantum wells (QWs) based vertical-cavity surface-emitting lasers (VCSELs) using MATLAB simulations. Numerical simulations were performed how varying N content affects cavity design and optical confinement properties. The stopband behavior and resonant wavelength shift were analyzed through reflectivity spectra for different N concentrations. The results reveal a red shift in the resonance wavelength and redistribution of the electric field intensity within the cavity as the N concentration increases. Higher N concentrations reduce mirror losses. However, excessive N incorporation introduces scattering and interface imperfections, leading to increased internal losses. In addition, an increase in N percentage results in reduced optical confinement factor and material gain decrease, indicating a lower overlap between the optical field and the active area. Both the threshold density and transparency carrier density were observed to rise with N addition, implying a decrease in recombination performance. Reflection-mode spectra confirm a red shift and a reduction in gain peak with higher N content. This analysis focused on a critical design of GaNAs VCSEL structure, which makes a distinction between engineering index profiles for optimal gain overlap and adjusting the optical path length for spectral control. A N concentration in the range of 1–2% provides the optimal balance along gain enhancement, optical confinement, loss minimization, and reflection gain, which are (0.11, 0.099), (175 cm−1, 162 cm−1), and (20.26 dB, 19.66 dB), respectively, thereby providing the overall performance of GaNAs/GaAs QWsVCSELs.
Copyright (c) 2026 Amanj L. Shafiq, Faten A. Chaqmaqchee, Mohammad Gh. Faraj https://creativecommons.org/licenses/by-nc-sa/4.0
Defining Characteristics of National BIM Adoption
Building information modeling (BIM) has become a transformative force in the Architecture, Engineering, and Construction industry, streamlining project coordination, reducing costs, and promoting sustainability. Governments worldwide are mandating BIM to enhance efficiency and digital transformation in public projects. However, a roadmap of national adoption requires clear regulations, standardized frameworks, and skilled professionals. This study critically examines policy frameworks, legal considerations, and workforce readiness, educational strategies, identifying key enablers and barriers to national BIM implementation. Utilising a qualitative methodology, the research reviews global BIM adoption models, educational strategies, and legal challenges that serve the BIM adoption in Kurdistan Region. Findings emphasize the importance of BIM maturity models, structured training programs, and data interoperability. Recommendations include developing national BIM standards, integrating digital permitting systems, and fostering public-private collaboration. The Kurdistan Region faces regulatory gaps that hinder BIM validation and industry-wide adoption. Addressing these challenges through the Kurdistan National BIM Standards, aligned with global benchmarks such as ISO 19650 and BSI PAS 1192, will drive digital transformation, enhance project efficiency, and position Kurdistan as a leader in digital construction.
Copyright (c) 2026 Dilan M. Rostam, Dawood S Atrushi, Dawa A. A. Masih, Diman N. Abdulqader https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence-Driven Network Slicing
Utilizing the artificial intelligence (AI) supported by software-defined networking and network function virtualization has a significant impact on the performance, flexibility, and efficiency in the development of 6G network slicing. This article compares AI-driven 6G slicing networks with traditional rulesbased 5G networks, focusing on latency, data throughput, jitter, power efficiency, and bandwidth. NS-3 and MATLAB have been utilized to evaluate the networks performance. The comparison results show that AI-driven network slicing reduces average latency by 50%, boosts data throughput by 40–90%, reduces jitter by 50%, and improves power efficiency by 20–28% compared to 5G networks. These results indicate that AI-powered network slicing in 6G networks outperforms traditional methods, enabling trendier network management. This sets a standard for network segmentation research in the future deployment of 6G networks.
Copyright (c) 2026 Ahmad B. Al-Khalil https://creativecommons.org/licenses/by-nc-sa/4.0
Fluorescence Quenching of the Fluorophore Diphenylamine for Determination of Neodymium (III)
Advanced analytical techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) are powerful tools for chemical analysis; however, they present several practical challenges, including high operating costs, the need for skilled personnel, and time-consuming procedures for analysis and data interpretation. Their performance can also be affected by sample matrix interference, limited throughput, strict calibration requirements, and environmental concerns related to hazardous solvent disposal. Therefore, the development of a simple, sensitive, and cost-effective method such as fluorometry is highly desirable. In this study, fluorometry was applied for the determination of neodymium (III) based on the quenching mechanism of the fluorescent agent diphenylamine (DPA) in an ethanol medium. Neodymium, a lanthanide element, is important due to its environmental, industrial, and technological impacts. The fluorescence signal of DPA, excited at 310 nm, is quenched by Nd(III) in the presence of 2.2 mL of Britton–Robinson buffer at pH 3.0, forming a stable ion-associated complex. The method showed linearity over the concentration range of 0.5–24.0 μg/mL, with a limit of detection of 0.046 μg/mL and a limit of quantification of 0.140 μg/mL, and a strong correlation coefficient (R² = 0.9956). The tolerance of common coexisting substances was evaluated, and the results were consistent with those obtained using ICP-MS, confirming the method’s accuracy and precision, indicating that it is a reliable, simple, and effective tool for environmental monitoring and analysis.
Copyright (c) 2026 Tara F. Tahir https://creativecommons.org/licenses/by-nc-sa/4.0
Experimental Investigation on The Gasification of High-Ash Indian Coal and Biomass in a Bubbling Fluidized Bed using Air and Steam
India produces significant amounts of biomass waste, including sugarcane bagasse and sawdust, as well as low-grade, high-ash coal – resources that are not fully tapped into despite their considerable energy potential. Gasification provides a clean and efficient technique for transforming these raw materials into valuable energy sources. This research outlines the design and development of a bubbling fluidized bed gasifier capable of handling high-ash coal (46% ash) and biomass, utilizing air and steam as gasifying agents. Experiments were performed to assess the effects of equivalence ratio (ER), temperature, and steam-to biomass (S/B) ratio on syngas composition. Raising the temperature from 500°C to 700°C significantly enhanced H2 and CO production across all feedstocks, indicating improved gasification efficiency at high temperatures. H2 concentrations increased from 6.7% to 9.1% for sawdust, 10.3–17.5% for bagasse, and 11.4–15.2% for high-ash coal. The optimal ER values for enhancing syngas quality were determined to be 0.35 for biomass and 0.43 for coal. Increasing the S/B ratio from 0.5 to 0.7 significantly increased H2 content, achieving 25.1% for sawdust and 23.3% for bagasse, attributed to the water-gas shift reaction. The energy balance analysis indicated energy outputs of 12.13 MJ/kg for sawdust, 16.79 MJ/kg for bagasse, and 17.72 MJ/kg for high-ash coal. These results validate the technical feasibility and operational versatility of the gasifier for various feedstocks. The research provides essential insights for optimizing gasification parameters, aiding in cleaner energy production and more efficient use of biomass and coal resources in India .
Copyright (c) 2026 Priyank Dave, Jigisha K. Parikh, Salim A. Channiwala https://creativecommons.org/licenses/by-nc-sa/4.0
Documentation and Classification of Heritage Buildings’ Styles through Machine Learning
Duhok, a city in Kurdistan Region of Iraq, has important historical and cultural evident in its archaeological sites. The city center contains many neglected heritage buildings that necessitate documenting as a crucial preliminary step for their preservation, facilitating a comprehensive understanding of their architectural, historical, and social significance. Consequently, the study aims to document buildings in the study region employing several documentation methods and looking at various aspects, such as architectural, historical, social, and cultural dimensions. The second goal of the study is to build an automated model utilizing advanced machine learning techniques, such as convolutional neural networks, transfer learning, and neural architectural search, to create a robust model for identifying and classifying architectural styles across various regions of the Kurdistan Region. The initials result of the study shows the unique attributes of Islamic, vernacular, modern, and postmodern architectural styles within Duhok’s legacy. The machine learning categorization of the model is very accurate, highlighting its potential as a reliable analytical tool for identifying and classifying architectural styles in Duhok city and across Kurdistan Region.
Copyright (c) 2026 Cebar J. Sadiq https://creativecommons.org/licenses/by-nc-sa/4.0
Broadband Ultra-high Frequency Sensor for Multi-condition Partial Discharge Detection in High-voltage Equipment
Partial discharge (PD) is a localized electrical phenomenon occurring within degraded insulation of high-voltage (HV) equipment, where its discharge mechanisms and radiated electromagnetic characteristics vary significantly across air, oil, and gas-insulated systems. Undetected PD can gradually damage insulation and eventually lead to equipment failure and power outages. Therefore, researchers are urged to develop reliable detection methods that are effective across different insulation media to ensure the safe operation of power systems. In this study, ultra-high frequency electromagnetic signals generated from PD activity are analyzed using an external log-periodic dipole array (LPDA) antenna. This research focuses on broadband sensing and considers the PD activity in various insulation materials, including air, oil, and gas. Moreover, lumped inductors are added to achieve a more compact antenna design without compromising the performance. The proposed LPDA antenna is fabricated on an FR-4 substrate and evaluated using a vector network analyzer. As a result, a reflection coefficient below −10 dB from 0.4 GHz to 2 GHz with a directional gain of 4–6 dBi is obtained, which satisfies the IEC 62478 standard. The results demonstrated that the proposed miniaturized LPDA antenna is effective for PD detection in different insulation environments.
Copyright (c) 2026 Zulbirri Faizol, Farid Zubir, Salah I. Yahya, Mohd H. Ahmad, Norhafezaidi M. Saman, Osman Ayop, Arshad K. Vallappil, Mohamad Z. Abd Aziz https://creativecommons.org/licenses/by-nc-sa/4.0
Elemental and Organic Matrix Analysis of the Land Snails Monacha cartusiana and Cochlicella barbara Shells
In this study, the occurrence of two introduced terrestrial gastropod species: Monacha cartusiana (O.F. Müller, 1774) and Cochlicella barbara (Linnaeus, 1758), in the Kurdistan Region of Iraq, was investigated. Species identifications were made based on snail morphology. Snail shells were studied to determine their chemical composition and its effect on their success and establishment in the study area. Their organic components (protein and chitin) and inorganic matrix (elements) were analyzed and their correlations were investigated. Elemental analysis was performed using inductively coupled plasma - mass spectrometry and energy dispersive X-ray fluorescence for both species and their surrounding soil. Gravimetric analysis revealed that the main shell component was calcium carbonate (CaCO3) in the form of aragonite, comprising over 96% of the shells’ mass. Proteins and carbohydrates accounted for <4% of the shells. Fourier-transform infrared spectroscopy spectral analysis confirmed the presence of chitin in its β-allomorph form. Thirty-eight elements were detected. Of which, potassium (K), strontium (Sr), and rhenium (Re) were significantly different between the two species. A highly significant correlation was found between protein and carbohydrates, and CaCO3 indicating the role of the organic matrix in the formation of the mineral component. The survival of the studied species is partly attributed to a considerable overlap in their organic and inorganic chemical characteristics, along with notable differences.
Copyright (c) 2026 Hero S. Rahman, Dotsha J. Raheem, Fenik Sh. Hussen, Hawbash H. Karim, Shelan M. Khudhur, Faten A. Chaqmaqchee https://creativecommons.org/licenses/by-nc-sa/4.0
Sustainable Removal of Methyl Orange from Wastewater Using Cyperus rotundus
Synthetic dyes found in wastewater create substantial environmental hazards and health risks, which require effective and sustainable wastewater treatment methods. This study investigates the use of Cyperus Rotundus as a natural adsorbent to remove methyl orange in aqueous solutions under a batch system. A total of 71 experimental runs were conducted to examine the effects of initial dye concentration (5–100 ppm), pH (3–11), contact time (10–90 min), and temperature (25–45°C) on the adsorption process. The maximum removal efficiency of 97% was obtained at a dye concentration of 10 ppm, pH of 4, and contact time of 90. Isotherm analysis revealed that the Langmuir model can provide the best fit with a maximum adsorption capacity of 2.25 mg/g, and a coefficient of determination (R2 = 0.657) while kinetic data followed the pseudo-second-order model indicating that chemisorption is the dominant mechanism (R2 > 0.95). Thermodynamic analysis proved that this adsorption is both spontaneous (with a standard Gibbs free energy change ranging from ΔGo = - 4.76 to -5.69 kJ/mol) and endothermic (enthalpy change ΔH° = 9.10 kJ/mol). Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to confirm the presence of dye adsorbent interactions by testing structural and functional changes of the groups that occurred after adsorption. The results show that Cyperus Rotundus is a low-cost biosorbent in methyl orange removal and can serve as a sustainable wastewater treatment material.
Copyright (c) 2026 Dunya I. Saleh, Ahmed A. Ahmed , Rawezh M. Mustafa, Bahra J. Swara, Ali Al-Wakeel, Dastan S. Abdul https://creativecommons.org/licenses/by-nc-sa/4.0
Breast cancer metastasis is one of the most consequential clinical problems in breast cancer, as it is responsible for most of the breast cancer attributable deaths in women globally. Timely detection of metastasis progression is crucial for improving therapies and enhancing patient survival. Although deep learning approaches, especially convolutional neural networks (CNNs), still fail to model global dependencies, they cannot work under weak supervision, and unable to generate interpretable predictions. Transformer-based models, on the other hand, provide a deeper contextual knowledge; but they are insensitive to local patterns and require large datasets. To overcome these issues, we present MetaBreastAI, an explainable dual-stream deep learning framework comprising a CNN branch fused with the convolutional block attention module and a Transformer stream. Each of these parallel branches captures spatial and contextual features, respectively, which are fused by a multi-instance learning approach for partially supervised classification. The proposed model has been tested on benchmark computed tomography scan dataset, it enables identifying one of the distinct features of MetaBreastAI and apply feature attribution methods to both visual and quantitative experiments, demonstrating that MetaBreastAI achieves better performance (macro-averaged F1-score: 91.7%; area under the curve: 96.1%) compared to each branch independently, and hybrid baseline models. By highlighting the lesion’s location, the heatmap supports interpretability. Due to the explainable detection of lesions in metastatic cases, our hybrid model provides a scalable and clinically feasible strategy through overcoming the downsides of earlier research, then it improves the reliability of AI-assisted techniques in medical decision-making.
Copyright (c) 2026 Bnar M. Ghafour, Abbas M. Ali https://creativecommons.org/licenses/by-nc-sa/4.0