University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Synthesis Of Monometallic (Ni) And Bimetallic (Mn–Ni) Nanoparticles Using Vigna Unguiculata Extract for The Adsorption of Congo Red Dye 1 17 244492 10.24271/psr.2026.5612603 EN Lawand L. Mustafa Technical College of Zakho, Duhok Polytechnic University, Zakho 42002, Kurdistan Region, Iraq. Amin K. Qasim College of Science, University of Zakho, Zakho 42002, Kurdistan Region, Iraq. Journal Article The green synthesis process for Ni and Mn–Ni bimetallic nanoparticles by using Vigna unguiculata seed extract have been investigated for the Congo Red dye adsorption. The aim of the study is to prepare these nanoparticles in more eco-friendly conditions and examine their performance for dye removal from aqueous medium.The plant Vigna unguiculata was selected due to its already proved richness with natural reducing as well as stabilizing agents, especially it is containing of phenolic compounds, flavonoids, and some other bioactive metabolites. These substances have been mentioned in several previous studies for their helping role in formation of nanoparticles, mainly through redox reactions and providing surface passivations. The synthesis was resulted in formation of good-crystallized nanoparticles, with average size about 30 nm for Ni and 22 nm for Mn–Ni particles. The prepared bimetallic Mn–Ni nanoparticles was achieved 83% of dye removal efficiency, which is comparing with 57% for only Ni, giving about 26% enhancement in adsorption activity. The optimum condition was found at 25°C, dye concentration 8 mg·L⁻¹, catalyst amount 0.005 g, neutral pH 7, and contact time of 60 minutes. These findings indicate that the bimetallic nanoparticles is having higher adsorption capacity under suitable experimental conditions.Whereas equilibrium data fits the Freundlich isotherm, kinetic analysis verified pseudo-second-order behavior. With (ΔH, ΔS, and ΔG) all negative thermodynamic parameters, the adsorption process was spontaneous and exothermic. While Ni nanoparticles showed greatly decreased reusability, with only 13% efficiency in the third cycle, Mn–Ni nanoparticles retained 51% of their adsorption efficiency after the third reuse cycle. Mn-Ni nanoparticles have a mean zeta potential of -33.8 mV, indicating strong negative surface charge and colloidal stability. These results support the feasibility of Vigna unguiculata-mediated synthesis as an environmentally friendly and reasonably priced technique for manufacturing recyclable adsorbents in wastewater treatment.

https://passer.garmian.edu.krd/article_244492_7dc3c585958625c2f8b870a811d42494.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Urban Growth Influence on Green Spaces Using NDVI And NDBI Analysis: Erbil City District as a Case Study 18 33 244496 10.24271/psr.2026.244496 EN Sweyda Azeez Department of Architecture, College of Engineering, Salahaddin University, Erbil 44001, Kurdistan Region, Iraq. 0000-0002-3813-2135 Journal Article Urban development is the result of population migration to the city. Therefore, sustainable development guidelines and strategic city planning are crucial for city expansion to avoid air pollution, PM value, and improve public health. Exploring the influence of rapid urbanization on Erbil city’s green area. This study explores the effects of PM and air pollution reduction on public health. Physical changes in the city can be directed from 2000 to 2024 across the entire year, including all seasons, by using GIS (Geographic Information System) techniques. The city growth assessment using NDVI and NDBI index was provided with a statistical analysis. The result shows that both indices are not growing in harmony, resulting in multiple threads, including fragmented green spaces, street canyon impact on ventilation, urban island heat, and the vegetation condition as diseased plants, all of which increase air pollution and PM, causing public health. The results suggest that the government should include a green wedge in Erbil's master plan to create connectivity between green spaces and improve airflow. The evidence suggests that the amount, connectivity, and seasonal characteristics of green areas are crucial factors in shaping urban structure. These green space elements are likely to decrease air pollution, resulting in a significant improvement in public health. It concludes with the key techniques for developing tangible, practical solutions for the successful deployment of the green wedge. https://passer.garmian.edu.krd/article_244496_1bbbf348021cef096ce2e417d185eea6.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Mechanical Properties of Concrete with Waste Glass and Waste Ceramic Materials 34 50 244497 10.24271/psr.2026.244497 EN Naema S. Saleh Department of Highways and Bridges Engineering, Technical College of Engineering, Duhok Polytechnic University (DPU), Duhok, Duhok 42001, Kurdistan Region, Iraq. Lawend K. Askar Journal Article Many researchers are currently attempting to make green concrete by substituting waste material. Utilizing hazardous industrial waste in concrete production will improve the environment. In current building construction, the use of sustainable alternatives is growing gradually. Waste glass and ceramic tiles when finely grounded create pozzolanic materials when incorporating into concrete it assesses the mechanical properties of concrete and reducing landfill waste by reusing materials in concrete production. In this study, waste glass was substituted for Portland cement at rates of 5%, 10%, and 15%. Additionally, crushed waste ceramic tiles were substituted for natural fine and coarse aggregates at rates of 10%, 20%, and 30%, respectively. A total of 90 cubes and 90 cylinders were cast. The cubes measured (15 × 15 × 15) cm while, the cylinders had a diameter of 15 cm and a length of 30 cm. The concrete specimens were examined for compressive strength, splitting tensile strength, and ultrasonic pulse velocity at 3, 7, and 28 days. The primary objective was to identify the optimal replacement levels that enhance strength performance and promote sustainability. The findings demonstrated that, in comparison with normal concrete, the adding waste glass with 10% enhancing the mechanical properties of concrete and 20% tile ceramic is ideal percentage for replacing with fine and coarse aggregate.

https://passer.garmian.edu.krd/article_244497_79abce93af71b2daf5890f7e54dcdc29.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Deep Learning for Facial Beauty Prediction: Integrating Transfer and Multi-Task Learning 51 63 244498 10.24271/psr.2026.244498 EN Ali Hikmat Ibrahem Department of IT, Technical College of Informatics - Akre, Akre University for Applied Sciences, Kurdistan Region – F.R. Iraq, 0009-0000-2829-4707 Adnan Mohsen Abdulazeez Technical College of Engineering, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. Journal Article Facial beauty prediction is a complex and subjective task with significant applications in cosmetic surgery, virtual makeovers, social media filtering, and personalized beauty recommendations. The perception of beauty varies across individuals and cultures, making it challenging for computational models to generalize effectively. conventional approaches based upon handcrafted features and statistical models often fail to capture the intricate patterns of facial aesthetics, limiting their adaptability to diverse populations. To address these challenges, this study proposes an advanced facial beauty model that integrates transfer learning with multi-task learning for improving this predictive accuracy and generalization. The EfficientNetV2B0 architecture is employed to leverage its superior feature extraction capabilities and memory efficiency, whereas multi-task learning is utilized to jointly predict beauty scores alongside auxiliary tasks such as gender and ethnicity classification. This joint learning enables the model to learn shared representations and capture subtle aesthetic features more effectively. Experiments on the SCUT-FBP5500 benchmark dataset show that the proposed approach outperforms single-task learning models, achieving higher Pearson correlation coefficients and lower mean absolute errors. multi-task model incorporating beauty score, ethnicity and gender prediction achieve a Pearson correlation coefficient of 0.9185, mean absolute error of 0.2068, and root mean squared error of 0.2762, surpassing existing state-of-the-art methods. These findings suggest that integrating transfer learning with multi-task learning significantly enhances facial beauty prediction, facilitating for more robust and generalizable computational aesthetics models with real-world applications in automated beauty assessment.

https://passer.garmian.edu.krd/article_244498_56f5ae2689bfffcd104b67137ce0be51.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Impact of Road Median Characteristics on Safety Performance: A Review Study 64 75 244499 10.24271/psr.2026.244499 EN Jiman Naji Hasan Highway and Bridge Department, Technical College of Engineering, Duhok Polytechnic University (DPU), Duhok 42001, Kurdistan Region, Iraq. Nasreen Ahmed Hussein nasreen.hussein@uod.ac Journal Article Roadway crashes remain a major global concern, accounting for nearly one-third of all traffic-related fatalities, particularly in mountainous regions. This study reviews and analyzes the impact of road median barrier characteristics on traffic safety, focusing on their role in minimizing crash frequency and severity. A systematic literature review was conducted to evaluate different median barrier types, materials, and configurations. Additionally, a case study was carried out to assess the effectiveness of cable median barriers in improving safety performance. Key findings from the literature review include that raised medians reduce overall crash frequency by 39%. The case study revealed that cable median barriers demonstrated high effectiveness, preventing over 90% of vehicles from crossing the median in the event of a collision. The review identifies a significant correlation between median design and crash outcomes, indicating that raised medians can reduce crash frequency by approximately 39%. The case study findings show that cable median barriers prevented over 90% of vehicles from crossing the median during collisions. The novelty of this work lies in its combined assessment of both literature and field data to provide a comprehensive understanding of median barrier efficiency and to highlight practical design implications for safer roadway systems.

https://passer.garmian.edu.krd/article_244499_fd766d93da601dba0690b362ffbe6c83.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Millimetre Wave Transmission Improvement Using Massive MIMO in Modern Communications System 76 83 244500 10.24271/psr.2026.244500 EN Rosin R. Kareem Department of Electrical Engineering, College of Engineering, University of Mosul 41002, Mosul, Iraq. Saad A. Ayoob Department of Communications and Intelligent Digital Systems Engineering, College of Engineering, University of Mosul 41002, Mosul, Iraq. Omer M. Ali Journal Article The massive multiple-input multiple-output (mMIMO) technique forms a fundamental component of fifth-generation (5G) communication systems, as it significantly enhances network capacity by integrating a large number of antennas within limited physical space. Initially developed for sub-6 GHz frequencies, mMIMO has also been extended to support millimeter-wave (mmWave) bands that operate within 30–300 GHz and provide extremely high data rates but limited coverage. This study examines the performance of mMIMO across both sub-6 GHz and mmWave frequencies, emphasizing its capability to improve mmWave transmission efficiency. The simulation-based evaluation considers non-line-of-sight (NLOS) conditions and benchmarks mMIMO against conventional MIMO systems. The obtained results demonstrate that mMIMO enhances the signal-to-noise ratio (SNR) by up to 85.5% at a distance of 100 m for 10 users, with an average improvement of 56.39% across various distances. Nonetheless, performance benefits gradually decrease as the number of users grows, revealing a trade-off between scalability and efficiency. Furthermore, the study explores beamforming mechanisms, channel modeling, and antenna array configurations, providing valuable insights for optimizing mMIMO deployment in practical 5G scenarios.

https://passer.garmian.edu.krd/article_244500_98d612e7a889a4052f8377daf84ef1d6.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Enhancement of Cooperative NOMA Performance via Integration withm-MIMO and Caching 84 94 244501 10.24271/psr.2026.244501 EN Salar Ismael Ahmed Erbil Polytechnic Universi1 Department of Information Systems Engineering, Erbil Technical College, Erbil Polytechnic University, Erbil 44001, Kurdistan Region, Iraq. ty 0000-0002-2644-2689 Siddeeq Yousif Ameen Department of Cybersecurity Engineering, Technical College of Engineering, Dohuk Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. Journal Article The demand for data and the spectrum utilization together with interference issues and energy use requirements and sustainability objectives and reduced latency needs have become the main driving forces behind developing 6G mobile networks. High-definition videos along with augmented reality and virtual reality and connected devices in the Internet of Things (IoT) are main factors behind the network growth. This research aims to examine the strategic combination of C-NOMA cooperative access and massive MIMO antennas with caching data entities to boost the operational qualities of these networks. The combined system seeks to manage escalating data volumes better. This research proposed a combination of C-NOMA with massive MIMO along with caching functions to boost 6G network operations in dynamic form. NYSIM along with MATLAB simulations are used to evaluate the performance of this system against different setups that implement massive MIMO technology in 6G communication networks. The results demonstrated that integrating C-NOMA with caching and m-MIMO leads to better performance in sum rate, latency reduction, and throughput for different file sizes, different networks traffic, different users and stations numbers and different power allocation levels. The results also show improvement gained in sum rate, throughput, latency reduction when compared with other recent studies too. Finally, integration results lead to higher-level performance improvement in the 6G mobile networks.

https://passer.garmian.edu.krd/article_244501_3a7d2b9ced875bc18af66a56be41a325.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Design of a High Gain Linear Power Amplifier for 2.4 GHz ISM Band Applications 95 108 244502 10.24271/psr.2026.244502 EN Firas M. Ali Department of Electronic Engineering, Faculty of Electrical Engineering, University of Technology, 10066, Baghdad, Iraq. Journal Article High gain linear power amplifiers are needed in modern industrial, scientific, and medical (ISM) systems to increase the transmission range and to produce distortion-free signals. The aim of this paper is to develop a design approach for a multi-stage linear power amplifier that can be used in the 2.4-2.5 GHz ISM band. The amplifier chain consists of three stages to raise the input power level from 0 dBm up to 39 dBm with an overall power gain of 39 dB. The first stage is designed using a medium power GaAs pHEMT transistor by means of the scattering parameters, while the second and third stages are synthesized based on a commercial GaN HEMT power device using the load/source pull technique. Microstrip lines have been used in the implementation of the matching networks to minimize the effects of parasitic elements and to reduce the power losses. The simulation results show an overall DC-to-RF efficiency of 55%, and 38 dBm output RF power at the 1-dB gain compression point (P1dB) across the band of interest. The simulated second harmonic distortion is better than 38 dBc, and the adjacent channel power ratio (ACPR) is high enough at the desired center frequency. These results confirm that the proposed amplifier circuit provides high gain and acceptable efficiency in addition to improved linearity.

https://passer.garmian.edu.krd/article_244502_3e84dc444a1c7f709a80ee3723ffeb6b.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Synergistic Effects of ZnO Nanoparticles and Environmental Factors on Biopolymer Solutions for Oil Recovery via Core Flooding 109 122 244503 10.24271/psr.2026.244503 EN Mohammed Khairy M.Salih Department of Petroleum Engineering, University of Zakho, Zakho 42002, Kurdistan Region, Iraq. Sherwan Simo Engineering Research Center, University of Zakho, Zakho 42002, Kurdistan Region, Iraq. Abbas Khaksar Manshad Department of Petroleum Engineering, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology 63187-14317, Abadan, Iran. Journal Article This study investigates the rheological impact of salt content and zinc oxide (ZnO) nanoparticles on guar gum and xanthan gum biopolymer solutions, both individually and in blends, for potential application in enhanced oil recovery (EOR). A series of shear viscosity measurements were conducted over a wide range of shear rates, temperatures, and polymer concentrations (400–2400 ppm) to evaluate how these additives influence fluid behavior under reservoir-like conditions.The results demonstrate that guar gum viscosity increases with salinity at both low and high polymer concentrations. In contrast, xanthan gum solutions exhibit high sensitivity to salt, resulting in a significant reduction in shear viscosity. Evaluation of xanthan/guar mixtures at ratios of 1:1, 1:2, and 2:1 revealed no noticeable synergistic effect. Furthermore, although salinity strongly influenced the rheological behavior of most biopolymer–ZnO systems, formulations containing a higher proportion of guar gum remained relatively stable.The findings suggest that a combination of xanthan and guar solutions integrated with 0.3 wt% ZnO nanoparticles provides a promising strategy for controlling fluid morphology and rheological properties in EOR applications. In addition, at 2400 ppm xanthan concentration, the resistance factor (RF) and residual resistance factor (RRF) exhibited very high values of 1504.08 and 784.8, respectively. Conversely, at 1200 ppm guar gum concentration, the RF and RRF values were significantly lower, at 23.64 and 19.08, respectively.

https://passer.garmian.edu.krd/article_244503_cd2beb0dd88ead7a0e16bb41a2221d1d.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 A Hybrid Deep Learning Approach for Influential Nodes Identification in Complex Networks 123 134 244504 10.24271/psr.2026.244504 EN Mohammed A. Ramadhan Department of Computer Science, College of Science, University of Zakho, Zakho 42002, Kurdistan Region, Iraq. Abdulhakeem O. Mohammed Department of Computer Science, College of Science, University of Zakho 0000-0002-0500-0398 Journal Article The influential nodes in complex network are the key of high effective information spreading. Several techniques have been developed for the discovery of such nodes, including centrality-based approaches, machine learning-based approaches, and deep learning-based approaches. This paper proposes CNNG, a novel hybrid deep learning model combining Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) for predicting node influence. CNNG processes each node’s local subgraph through parallel CNN and GAT components to capture both structural patterns and relational dependencies. The model is trained in a supervised regression setting, using node influence scores generated via SIR simulations. Experiments on twelve real networks show that CNNG achieves the best average Kendall’s Tau coefficient of 0.7510, outperforming the second-best method by 2.85% as well as the highest average Monotonicity Index of 0.9954 and the highest average Jaccard Similarity of 0.7298 over ten networks. Further, CNNG provides these results in a reasonable amount of computing time, which demonstrates the practicability, efficiency and generality of CNNG for the complex network analysis.

https://passer.garmian.edu.krd/article_244504_de4877275ade8ca5db1738e33e56e930.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Towards Transparent Decisions: CNN Ensemble with XAI-Driven Interpretations 135 148 244505 10.24271/psr.2026.244505 EN Ahwaz Darweesh Hayder Information Technology Management Department, Technical College of Administration, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. Jwan Najeeb Saeed IT Department, Technical College of Duhok Duhok Polytechnic University, 0000-0001-7829-3139 Journal Article Skin cancer remains a global health threat with increasing incidence rates. Accurate and early classification of skin lesions into benign or malignant types is essential for timely treatment and prevention of severe outcomes. In this paper, we present a comprehensive deep learning-based framework that leverages three benchmark datasets—PH2, ISIC (Benign vs Malignant), and HAM10000—using transfer learning and ensemble techniques. Pre-trained models including VGG16, ResNet50, and EfficientNetB4 were fine-tuned on each dataset, and majority voting was employed to combine predictions. The Gradient-weighted Class Activation Mapping (Grad-CAM) was also used to improve visual explainability. The findings demonstrate a notable increase in classification accuracy, surpassing current techniques and reaching over 98% accuracy on certain datasets. This study highlights the impact of hybrid architectures and explainable AI in advancing the state of skin cancer diagnosis systems.

https://passer.garmian.edu.krd/article_244505_d616955fa05eb76ee351b98e80b6b7c7.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 A Deep Learning Approach for Automated Kidney Tumor Classification 149 165 244506 10.24271/psr.2026.244506 EN Hivi Kamal Ismael Department of Information Technology, Akre University for Applied Science, Akre 42004, Kurdistan Region, Iraq. Wafaa Mustafa Abdullah Department of Cybersecurity Engineering, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. 0009-0007-5496-8745 Journal Article Correct classification of kidney tumors in computed tomography (CT) images is crucial for early diagnosis and treatment planning. In this study, we propose a hybrid architecture in which ResNet50 is employed for localized spatial feature extraction, while Vision Transformer (ViT) enables global contextual learning to automatically classify kidney tumors into multiple classes. A single-stage paradigm was then performed to classify CT images into one of the three clinically relevant categories: normal tissue, benign tumor and malignant tumor. All classifications and evaluations were performed at the slice level, where each axial CT slice was treated as an independent input sample. The model was independently trained and tested using two publicly available datasets of CT images, KAUH-Kidney, and CT-Kidney. It so adapts advanced preprocessing methods like class-aware data augmentation, normalization, and focal loss to deal with class imbalance. To thoroughly evaluate performance, we used a 5-fold cross-validation. On the KAUH-Kidney dataset, the hybrid model reached an accuracy of 99.53% and on the CT-Kidney dataset it detected 99.73% of the samples with the perfect macro and micro AUC scores on both datasets. Where the combined approach outperformed both standalone CNN or transformer-based architectures in accuracy, F1-score, and generalization. This study demonstrates the potential of hybrid deep learning frameworks to assist in the more accurate, efficient and automated classification of kidney tumors in clinical practice settings.

https://passer.garmian.edu.krd/article_244506_c70db8d4b89408ac011c844dfb606128.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Advanced in Strengthening Prestressed Concrete Beams: A Comprehensive Review of Modern Techniques 166 187 244507 10.24271/psr.2026.244507 EN Hawar Hasan Jasim Highways and Bridges Engineering Department, Technical College of Engineering, Duhok Polytechnic University (DPU), Duhok 42001, Kurdistan Region, Iraq. Yaman Sami Shareef Al-Kama Journal Article Prestressed concrete (PSC) beams are widely used in construction worldwide, especially for bridges due to their high strength, durability, and efficient load-carrying capacity. However, over time, these structures face deterioration from various factors, such as material aging, fatigue, environmental factors, and increased service loads, requiring strengthening methods. Many studies have investigated various materials and schemes for improving the flexural and shear capacities of (PSC) beams. This review aims to explain external strengthening techniques for (PSC) beams, including steel plate bonding, external post-tensioning method, Fiber-Reinforced Polymers (FRP), and shape memory alloys (SMAs). A review was conducted to investigate the effects of various strengthening methods on the performance of (PSC) beams, focusing on crack propagation modes and specimen failure related to flexural and shear improvement techniques. Flexural strengthening enhances bending capacity and serviceability by reducing deflections and improving load resistance. In contrast, shear strengthening techniques aim to prevent diagonal shear cracks and brittle failures near support zones. A comparative analysis highlights each method's advantages, limitations, and applicable contexts, highlighting their impact on structural performance and longevity. The discussion also includes these techniques' knowledge gaps, challenges, and constraints while proposing future research directions to improve their effectiveness. This review study offers significant insights for engineers and researchers focused on enhancing the performance and durability of (PSC) beams by recent strengthening techniques.

https://passer.garmian.edu.krd/article_244507_d5b151b0b1e1f8e651a45f6b4cd9959a.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Quercus Infectoria Counteracts Lead Toxicity in Carp: Growth, Metabolic & Enzymatic Protection 188 197 244508 10.24271/psr.2026.244508 EN Payman Mohammed Saleem Mohammed Saleem Department of Biology, College of Science, University of Zakho, Zakho 42002, Duhok, Kurdistan Region, Iraq. Basim. S. A. Al Sulivany Department of Zoology, Emerson University 60000, Multan, Punjab, Pakistan. 0000-0002-0117-7022 Muhammad Owais Oyegoke Rukayat Abiodun Department of Biochemistry, Faculty of Life Sciences, University of Ilorin 240003, P. M. B. 1515. Ilorin, Nigeria. Journal Article The research project aims to determine the protective effects of Quercus infectoria seeds (QIS) on lead nitrate (Pb(NO₃)₂) induced toxicity in Cyprinus carpio (common carp). Eighty juvenile carp weighted (151.4 ± 6.4 g) were divided into four groups: T0 (control), fed standard diet; the fish in the second group T1, exposed to 5 mg/L of Pb(NO₃)₂; T2, were fed a QIS-supplemented diet (10 g/kg); and T3, were exposed to Pb(NO₃)₂ + QIS. the parameters of the growth were severely influenced in T1, with final weight (171.1±4.748g) significantly lower than those present in the T0 (235.4±11.6g; p< 0.0001). On the other hand, QIS supplementation (T2) showed the highest growth (257.4±12.73g), as compared with both control and Pb-exposed groups (p< 0.0001). The T3 group demonstrated intermediate growth (223±12.36g), indicating QIS partially counteracted Pb toxicity. Biochemical analysis revealed that Pb-exposed fish (T1) developed hyperglycemia (77.37±4.628mg/dL when compared with T0: 65.4±1.65 mg/dL) and elevated urea (5.3±0.55mg/dL) as compared with the T0 group (3.733±0.66 mg/dL). In contrast, QIS groups showed improved metabolic profiles. Tissue enzyme activities (alanine transaminase (ALT), aspartate transaminase (AST), lactate dehydrogenase (LDH), and alkaline phosphatase (ALP)) in muscle and spleen confirmed Pb-induced damage, with QIS significantly reducing oxidative stress markers (p< 0.05). These results conclude that QIS effectively reduced the toxicity of Pb by elevating the growth parameters, protecting the tissue, and supporting its use as a natural feed additive in aquaculture.

https://passer.garmian.edu.krd/article_244508_1599fe6a746eb3d98073ff1ade6bf5d6.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Detection and Classification of Colorectal Cancer Types Using Deep Residual Learning based on ResNet-50 with Adam Optimization Method 198 213 244509 10.24271/psr.2026.244509 EN Herman Omer IT Department, Duhok College Technique, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. 0000-0003-0834-5524 Salwa M. Hasan Food Production and Technology Department, Duhok College Technique, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq. Lozan M. Abdullrahman Amira Bibo 0000-0002-5102-6193 Journal Article These days, digital pathology plays a crucial role in tumor detection and prognosis, particularly in objectively evaluating histological images for colorectal cancer (CRC). Deep learning models have demonstrated significant success in image classification, prompting researchers to adopt these methods for medical image analysis. This research explores deep learning methods, specifically using ResNet architectures (ResNet-18, ResNet-50, ResNet-101), combined with various optimization methods, including Adam, Stochastic Gradient Descent with Momentum (SGDM), and Root Mean Square Propagation (RMSProp), for classifying colorectal cancer types from histological images. A comprehensive evaluation was conducted to identify the most effective neural network architecture and optimization approach. Among the tested combinations, the ResNet-50 model with the Adam optimization method achieved the highest accuracy of 99.86% on the NCT-CRC-HE-7K dataset and an accuracy of 98.36% on the NCT-CRC-HE-100K dataset, outperforming other tested combinations and existing approaches in the literature, effectively differentiating between benign and aggressive colorectal cancer. Lastly, the suggested model was compared to the current models, which were assessed to ascertain the most effective neural network models and the best training approach for our case study on colon tumor segmentation.

https://passer.garmian.edu.krd/article_244509_49947d618251a5c79a0b76d844ab7def.pdf

University of Garmian Passer Journal of Basic and Applied Sciences 27065944 8 Special Issue 2026 08 01 Assessing the Accuracy of a Network of GPS Passive Stations in Duhok City 214 227 244510 10.24271/psr.2026.244510 EN Farsat Heeto Abdulrahman Department of Civil Engineering, University of Zakho, Zakho 42002, Kurdistan Region, Iraq. 0000-0002-6928-0851 Sarhat M. Adam Department of Civil Engineering, University of Duhok, Duhok 42001, Kurdistan Region, Iraq. 0000-0001-7145-2483 Yousif Y. Zaia Journal Article Surveying plays a critical role in engineering projects, particularly in obtaining precise positional data for construction areas. For this purpose, accurate Reference Control Points (RCPs) are essential. RCPs can be either permanent (constructed with concrete or other materials) or temporary (used for local projects and established using GNSS positioning principles). Although temporary RCPs can be accurately determined from GNSS data through statistical analyses, their reliability tends to degrade over time. In 2011, a German company (Vossing) established a network of RCPs to support the Duhok city master plan. These RCPs have since been widely used by surveyors and engineers; however, their positional accuracy has never been systematically evaluated. This study presents the first comprehensive assessment and refinement of the Duhok RCP network using modern GNSS online processing platforms OPUS, AUSPOS, and CSRS-PPP. The RCPs were re-measured, processed, and statistically analysed to determine positional shifts and uncertainties relative to their original coordinates. Results illustrated that the mean uncertainties of approximately 27 cm in the East, 37 cm in the North, and 2.5 cm in height, corresponding to a total shift of about 46 cm with an azimuth deviation of 53° 34′ 37.709″. The findings demonstrate that long-term reliance on outdated RCP coordinates can introduce significant positional errors. This research not only quantifies the current accuracy of the Duhok network but also introduces a replicable methodology for verifying and updating control networks in regions lacking continuous GNSS reference infrastructure.

https://passer.garmian.edu.krd/article_244510_2807dcc7d275d480fa0db4e503049b40.pdf