Zanco Journal of Pure and Applied Sciences

The harmful effects of different levels of Nickel nanoparticles (Ni-NPs) (particle size = 20nm) on male rats\' lungs were explored in the study. Intraperitoneal injection of Ni-NPs occurred at 0, 5, 20, and 100mg/kg/bw over 28 days. The quantity of malondialdehyde (MDA). The evaluation also included the levels of reduced glutathione (GSH) and the activity of antioxidant enzymes, including superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase (CAT). Additionally, observations were made on lung changes at the histopathological, immunological, and ultrastructural levels. Lung homogenates displayed a noticeable increase in MDA levels, which was dependent on the dosage. Dose-dependent changes were observed in the biochemical parameters. Histological analysis revealed several abnormalities in the rats treated with Ni-NPs, including thickening of alveolar walls. Pulmonary vessels experience blood congestion, lung parenchyma is severely damaged, and lung tissues suffer from hemorrhage. The diffuse appearance of the inflammatory leukocytes stood out as the most evident histopathological characteristic. A dose-dependent pattern was observed in all of these histopathological features. The p53 immunohistochemical demonstration exhibited a higher reaction intensity compared to the control. Examination of the ultrastructure showed that exposure to Ni-NPs led to damage in the mitochondria. The detected biochemical, histological, immunohistochemical, and ultrastructural alterations in the rat lungs demonstrate the harmful effects of NiNPs on lung health. These effects seem to be mediated in large part by the production of oxidative stress.

The present study investigates the subsurface conditions for a proposed small dam in Doly Rostey, situated in the extreme northeastern part of Erbil City within the Iraqi Kurdistan Region. To achieve this, four profiles were surveyed: one along the dam axis (NW-SE direction), two along the abutments, and one along the stream parallel to the abutments (NE-SW direction). Each profile spanned 355 meters, employing a Wenner-Schlumberger electrode configuration with 5-meter electrode spacing. Field data were processed and interpreted using the software “RES2DINV” (version 3.59.117), generating four detailed 2D resistivity inversion sections. The results revealed the Qamchuqa Formation as the dominant subsurface feature, with resistivity values of 500 to 60,000 Ω·m, indicative of dolomitic and limestone layers. The study also identified karstic features, fractured zones, and potential faults, highlighting structural complexity and pathways for water movement. Additionally, low-resistivity zones (45–500 Ω·m), corresponding to surface sediments and marly limestone within the uppermost 16–60 meters. These findings emphasize the site’s structural and lithological variability and highlight the critical need for detailed geotechnical and hydrological evaluations to ensure the dam\\\\'s stability and safety.

The sheep fleece serves as the primary source of carcass contamination. In this study, before and after slaughter, lactic acid was sprayed for microbial decontamination for lamb carcasses. Eighteen male lambs were randomly assigned to two groups: a treatment and control groups. Each treatment group consisted of six lambs that received sprays of 3% or 6% lactic acid in two distinct phases: (I) applied to a live animal's hide/fleece and (II) was applied to carcass surfaces immediately after slaughter. Remaining six animals, the no-spraying treatment was used as the control group. Microbiological samples were then collected from animal skins or fleece before spraying, and carcass surfaces 24 h after slaughter. The characteristics of the meat quality were also analyzed. Total aerobic counts, coliforms, Escherichia coli, Bacillus, Pseudomonas, and Staphylococcus were detected in both the external hide/fleece and the carcass surface samples. Nonetheless, treatments involving lactic acid sprays markedly decreased counts of all examined microbes on carcass surfaces compared with the non-sprayed control group. Treatments involving 3% and 6% lactic acid sprays reduced Escherichia coli and total coliforms counts by 2.36, 2.51, and 2.67, 2.71 cfu/cm2 of the carcass surfaces, respectively. Likewise, treatments spraying with 3% and 6% lactic acid treatments led to reductions of 4.20 and 4.31 cfu/cm2 for the populations of total aerobic count, 1.72 and 2.06 cfu/cm² for Pseudomonas, 2.02 and 2.06 cfu/cm² for Bacillus and 1.83 and 2.75 cfu/cm² for Staphylococcus, respectively. No significant differences were observed in the physicochemical sensory attributes between the control and spray treatments indicating that spraying lactic acid application pre-slaughter did not cause stress to the animals. Spray application of lactic acid pre- and post-slaughter stages is practical and can be used for optimizing food safety as it effectively ensures reduce bacterial contamination of lamb carcasses without negatively impacts the quality.

Penicillium digitatum is one of the major pathogens for green mold rot in citruses and is a serious threat for postharvest disease control. P. digitatum was isolated from the infected oranges, and pathogenicity tests were conducted. The most virulent isolate was then identified by molecular analysis and confirmed 100% is P. digitatum. The in vitro efficacy of cinnamon oil as a potential antifungal agent was studied using well diffusion and minimum inhibitory concentration assay. The findings confirmed that higher concentration (5%) had a significant inhibition 90% impact on fungal growth. The GC-MS analysis showed 29 compounds, of which six major compounds were present in the highest percentages: 5H-1-Pyrindine-3-carbonitrile, 2-bromo-6,7-dihydro- (24.52%), Tolmetin (13.9%), and Palmitic acid (8.82%), 6-Acetyl-6-indolo(2,3-b)quinoxaline (7.75%), 1-Methyl-1H-pyrazolo[3,4-b]pyridin-3-ylamine (24.25%), 2,5-Ethano-2H-azocino[4,3-b]indole (7.65%). For the in vivo experiment, orange fruits were immersed in 1% cinnamon oil to assess its efficacy in extending shelf life. Shelf life and infection reduction rate were observed for 22 days, which showed that cinnamon oil was effective in resisting fungal infections while ensuring fruit quality. These results indicate that cinnamon oil is a natural and eco-friendly substitute for chemical fungicides in plant disease management and food preservation.

This study was conducted from October 2022 to August 2023 at the College of Agricultural Engineering Sciences, University of Duhok, Kurdistan, Iraq, to determine the influence of foliar nutrient sprays on the vegetative growth, leaf mineral content, and yield traits of \'Rubygem\' strawberry plants.\"The treatments included three concentrations of each factor, Casmocal (Ca) (0, 2 and 4 ml.L-1), Power Mix bio fertilizer(PM) (0, 2 and 5 ml.L-1), and iron (Fe) in the form of Fe-EDDHA (0, 40, and 80 mg.L-1). The results demonstrated that Casmocalat4ml.L-1significantly improved some parameters, including total leaf chlorophyll content, leaf area, leaves nutrient content (P, K and Ca %) and yield per plant. However, the highest leaf total chlorophyll, leaf area, leaf dry matter percentage%, leaf nutrient content (N%, K% and Femg.L-1)and yield per plant were recorded when high level of Power Mix (5ml.l-1) was sprayed. Fe at 80 mg.L-1enhanced (leaf total chlorophyll, leaf dry matter percentage, leaves nutrient content (N%, P%,Ca% and Fe mg.L-1)and yield per plant. Additionally, the findings indicated that combination of calcium, Power Mix and iron had optimized enhancement on the growth and productivity of “Rubygem” strawberry plants.

The frequency and intensity of flash floods increased substantially in recent years in the world which are the major risk for urban regions and environment. The objective of this comprehensive study is to assess the buildings vulnerable to flood hazards in Erbil city, Kurdistan region of Iraq using the integrated approaches of Geographic Information Systems (GIS), satellite remote sensing, and the Analytical Hierarchy Process (AHP). 14 flood-contributing parameters were used to map flood susceptible regions in the Erbil city. Rainfall, aspect, topographic wetness index (TWI), elevation, flow accumulation, lithology, soil data, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land use land cover (LULC), slope, stream power index (SPI), drainage density, and distance to roads were the factors that have been used to create flood susceptibility map (FSM). The FSM map showed considerable results with the accuracy of the 92.2%, using historical flood data to assess the accuracy. The results showed that 109 km2 (12.1%) of Erbil city area located in high and very high flood risk areas. Moreover, the outcome underscored startling results, showing that nearly half of buildings in Erbil city (47.6%) were located in high or very high flood risk areas. Additionally, the finding showed that 33853 (3.84%) and 69258 (7.85%) of population in Erbil city were located in very high and high risk flood zones, respectively. This study offers valuable insights for policy makers and urban planners, significantly contributing in monitoring urban environments, early warning systems, flood mitigation strategies and sustainable urban growth.

The tremendous increase of data produced by Internet of Things (IoT) networks emphasizes how important to have robust security mechanisms in place to guarantee authenticity, integrity, and confidentiality. Due to inherent limitations of IoT devices, like limited memory, computing power, and energy consumption, they are particularly vulnerable to security threats and attacks. This paper comprehensively surveys possible cryptographic solutions to enhance IoT cybersecurity, discusses cryptographic algorithm classification, implementation problems, and performance parameters in the IoT ecosystem. This study explores common cryptographic optimization techniques using meta-heuristic algorithms (MHA), machine learning (ML), and blockchain (BC). A taxonomy of emerging technology solutions was demonstrated based on IoT security issues. Additionally, the paper explores cryptographic improvement challenges in the IoT environment with possible solutions.

Around the world, women are diagnosed with breast cancer, which is a deadly disease. Early detection is very important in helping improve the survival rate and advance the excellence of care for patients. With the help of deep learning models, medical image analysis can be performed with high accuracy and can even be used to automatically identify breast cancer. Several research has been undertaken on the application of survival analysis and deep learning models for the detection of breast cancer. This work introduces a methodology that can effectively and precisely diagnose breast cancer with reliability. This approach utilizes survival analysis and Deep learning, enhanced with statistical techniques such as the Cox regression model and Kaplan Meier Method. Additionally, a dataset of breast cancer magnetic resonance imaging (MRI) has been created and refined. The study findings demonstrated that the utilized model for the survival analysis of breast cancer exhibited excellent performance, achieving a remarkable accuracy rate of 98%. The improved Mask R-CNN with the scanner known as enabled the comparison of tumor size and breast size.

Gas Turbines (GT) are essential generation units in both centralized and distributed power systems. In many regions worldwide, GTs are the primary source of electrical energy due to their affordability, efficiency, and flexibility. GT can quickly reach peak output, making them ideal as auxiliary units, additionally, they are often integrated with renewable energy sources to improve system adequacy and reliability. Accurate forecasting of GT power output is crucial for achieving efficient management and smart operation of these units.

This study presents the first application of machine learning techniques for electrical power output prediction from a combined cycle gas power plant in the Iraqi Kurdistan region. A novel dataset comprising 7,893 data points collected during base load operations over four years, from 2019 to 2023 at the Perdawood Electrical Power Station in Erbil, Kurdistan Region of Iraq. The dataset includes eight features - Ambient Temperature (AT), Ambient barometric Pressure (AP), Specific Humidity (CH), grid frequency (DF), Gas Fuel Flow (GP), Compressor Pressure Discharge (CPD), Compressor Pressure Ratio (CPR), and Exhaust Temperature (TTXM), and the Electrical Power Output (EP) as the target variable.

The dataset has been preprocessed, and the Support Vector Regression (SVR) machine learning algorithm is applied to models and trains the dataset. The model's accuracy is carefully evaluated using the effective metrics such as Mean Squared Error (MSE), R-squared (R²), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results demonstrate the effectiveness of the Support Vector Regression (SVR) algorithm for forecasting the power output of gas turbines, that authenticates the practicability and value of applying machine learning to real-world GT operation in the Kurdistan region. This work provides a strong foundation for introducing a new strategy of smart energy management in the power sector in the region.

Visual comfort is a fundamental human goal impacted by the perception of interior architectural geometry via vision as one of five human sensors. The geometrical and spatial qualities of an architectural space, including area, size, dimension, volume, and human scale, among others, affect the perceived space. Furthermore, it is necessary to note that daylight influences the perceived indoor space. The mosque was the most important building in Islamic civilization because it serves as the principal place of worship. The prayer hall (or haram) is an essential part of the mosque. Mosque typo morphology is the focus of this research, which examines the building from a spatial and volumetric perspective. This study investigates how prayer hall's most popular typo-morphological characteristics may affect users' visual comfort. This study examines mosque typo-morphological design using mixed-method qualitative and quantitative evaluations. A qualitative method was used to describe the mosques through architectural drawings. A quantitative questionnaire survey was conducted to receive experts' feedback on mosque visual comfort satisfaction using VR. The study's findings indicate that creating spatial openness and visible volume in prayer hall configurations clear of obstacles and effective integration with monumental scale and vertical visual proportion significantly impacts visual comfort. The study concluded that the levels of visual comfort in mosque prayer halls differ according to the typo-morphological design characteristics of mosques. The current study contributed to linking the known mosque prototypes and the level of visual comfort for users through an in-depth comparative-analytical study.

Users’ experience on the internet is positively influenced by recommendation systems, which help them in navigating through a lot of information available to them. The accuracy and relevance of recommendations can be improved only if advanced techniques like GNNs are used due to the increasing complexity associated with user-item interactions. Detailed patterns in preferences and behavior are captured by GNNs which model relationships between users and items. To this end, we present a Position-Aware Neural Graph Collaborative Filtering (PA-NGCF) model with two key differences from previous graph neural network-based recommender systems. In terms of our primary contribution, we have come up with a new way of creating node embeddings that make use of limited rating information from users to adequately capture user-item interactions. Furthermore, during message passing phase in our model, the positional information about nodes within the graph structure is explicitly included enabling richer understanding about the role and significance of each node involved in recommendation process. Two real-world datasets were used for extensive experiments to show how effective our approach was. Our findings indicate that PA-NGCF method effectively reduces the rating prediction error within recommendation systems. This error has been evaluated using the MAE and RMSE metrics. Our findings highlight the potential of the proposed method in increasing the quality of recommendations and addressing cold start problems in sparse datasets, as the reduction in prediction error leads to more accurate ranking of items adapted to user preferences.

The Paved roads are one of the most important and basic infrastructure, and there management is a significant issue for pavement organizations and engineers. Pavement Maintenance Management System (PMMS), which is a part of the Pavement Management System (PMS), consists of scheduled activity to obtain powerful management and cost-effective maintenance based on the Pavement Condition Index (PCI). The purpose of this research is to evaluate the pavement condition and provide a systematic plan for preventive maintenance, rehabilitation, and reconstruction of pavement roads to preserve it at an allowable level of serviceability. In this study, PAVER 7.1.3 and ArcGIS 10.8 were used to develop pavement management models for the Ranya urban network. A total of 17 roads with a combined length of 27.84 km and an area 478,750 m2 were selected. The road network includes three arterial roads two collector roads and twelve local roads that compose 33% and 25% and 42% of the total roads respectively. To assess the type, severity, and quantity of distresses, visual inspection is carried out. Moreover, by using PAVER 7.1.3, the collected data are inventoried and assessed for calculation of PCI, prediction deterioration model, and PCI prediction. PAVER 7.1.3 is integrated with ArcMap to develop (PMMS) for the Ranya urban street network and layout the outputs like PCI, rehabilitation, and maintenance priority. The outcomes shows that the PCI of the total study area, have Fair condition 67.16, including 24.5% have Good, 20% have Satisfactory, 31.5% have Fair, 20% have Poor, and 4% have Very Poor condition. This indicates that roads in Poor and Very Poor condition require immediate maintenance. Maintaining Fair-condition roads can slow deterioration, while priority repairs should address poor areas to prevent costly reconstruction. Roads in Good to Satisfactory condition can be preserved through preventive maintenance.

In network analysis, the prediction of the connections or associations between entities or nodes within the network becomes important. Link Prediction is the problem of predicting or identifying the existence of a link between two entities in a network. However, it still the main issue in the complex network data application field, particularly in the type of analysis related to co-authorship networks despite its wide usage. Topological methods and content-based methods are the two different approaches that have been proposed for the link prediction in collaboration networks. However, topological methods are based on the structural analysis of the network, and content-based approaches rely on textual information from academic papers in the network. In this paper, we introduce the Content and Graph-Based Link Prediction (CGLP) approach, which integrates topological and content-based features from networks in a hybrid manner for predicting links in co-authorship networks. The efficacy of the proposed approach was already tested using three academic datasets: Hep-th, Hep-lat, and AMC by applying various machine learning models. Results indicated that all models showed almost the same efficiency on all three datasets and outperformed the state-of-the-art approach with a maximum F1 score of 98.05% and ROC AUC of 98.74%.

In today’s world, technology has facilitated the publication of fake news on social media. Thus, it becomes very difficult to differentiate real news from fake ones particularly in low-resourced languages such as Kurdish. Therefore, in this study, the author bridges this gap by proposing a hybrid deep learning model named “ACO-CNN” that can effectively recognize false news. The hybrid approach includes the Ant Colony Optimization (ACO) that has been utilized to optimize the Convolutional Neural Network (CNN) hyperparameters to enhance its capabilities to successfully detect fake news. The proposed method was evaluated against the state-of-the-art methods that were incorporated for the same purpose including, Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest, (RF) and Decision Tree (DT) using two existing benchmark fake news dataset namely “KurdFake” and “KDFND”. Our experimental results indicate that the ACO-CNN outperformed other approaches in terms of F1-score on both datasets. This work heavily contributes to the Kurdish Natural Language Processing (NLP) field and the development of effective deep learned based fake news detection tools for under-resourced languages.