Zanco Journal of Pure and Applied Sciences

The genus Myosotis incrassata Guss. is one of the largest genera of tribe Myosotideae, subfamily Cynoglossoideae, and family Boraginaceae. The aim of this study is to validate the M. incrassata is new record in Iraq, so that an extensive sampling characterization of macro- and micro-morphology, some characteristics of pollen grains, such as shape, color, size, sculpture, and number have been studied. To further validate the taxonomic position of M. incrassata within the Boraginaceae family, phylogenetic analyzes have been performed. The collected plant specimens differ from the other species that are already present in Iraq, where M. incrassata differs from others in that the calyces in fruit are rarely appressed, less than 3 mm, and not crowded. Pollens was yellow in color, single, 3-colporate, circular in equatorial view, spheroid in polar view, small-sized. The molecular studies have confirmed the genetic relationship and placement of this newly recorded species within the broader context of the family, solidifying its classification and evolutionary lineage.

This study involved collecting samples of 27 different rice varieties from local markets in Erbil, Kurdistan region, Iraq. Measurements were performed using the NaI (Tl) scintillation detector system. The specific activity of 232Th, 226Ra, and 40K radionuclides in the rice samples ranged (0.13-1.79) Bq kg-1, (0.16-8.3) Bq kg-1, and (77.1-180) Bq kg-1, respectively. The total annual effective dose (AE) in the rice samples ranged from 32.37 µSv y-1 to 116.42 µSv y-1, which obtained due to an ingestion of radionuclides via rice consumption, the values of AE were lower than the global value of 290 µSv y-1, as reported by UNSCEAR 2008. The excess lifetime cancer risk (ELCR) ranged from 0.113 to 0.407×10−4, below the acceptable value of 2.9×10-4, as reported by UNSCEAR 2000. In addition, the internal hazard index (Hin) ranged from 0.33 to 0.70 and the external hazard index (Hex) ranged from 0.025 to 0.046. Estimated radiological indices were below the overall limit value of 1. The study used multivariate statistical techniques including descriptive statistics, histogram and Pearson correlation analysis to investigate the relationship between specific natural radioactivity and associated radiological parameters. The study utilized multivariate statistical analysis techniques, including descriptive statistics, histograms, and Pearson correlation analyses, to investigate the relationship between the specific activity of natural radionuclides and the associated radiological parameters. Thus, the studied rice brands are radiologically safe as an essential foodstuff in Erbil Governorate.

The resize result involution graph of a finite group , is a simple undirected graph whose vertices are the conjugacy classes of and two distinct vertices are adjacent if their representative product is a non-trivial involution. In this paper, we describe an algorithm to obtain the resize result involution graphs. Also, we prove that the resize result involution graphs for , , the Janko groups , the Held group and the exceptional group 2 are connected with diameter at most and girth . We find some properties of the resize result involution graphs. The properties of these graphs are obtained by using GAP and YAGs packages.

CuO nanoparticles were synthesized via a green route using Curcuma longa extract as both reducing and capping agents. The influence of precursor concentration (0.025–0.1 M) on the morphology, size distribution, and structural quality of the nanostructures was systematically investigated. Characterization by SEM, XRD, FTIR, UV–Vis spectroscopy, and EDX identified an optimal sheet‑like morphology at 0.05 M (sample S2), with an average thickness of 18 nm, minimal aggregation, and high homogeneity. FTIR spectra confirmed characteristic Cu–O stretching vibrations, and XRD patterns revealed a phase‑pure monoclinic CuO crystal structure. UV–Vis analysis showed a distinct absorption peak at 376 nm corresponding to the optical band gap, while EDX confirmed elemental purity. These results demonstrate that Curcuma longa–mediated synthesis offers an eco‑friendly, scalable approach for producing CuO nanostructures suitable for catalysis, sensing, and environmental remediation applications.

Forest ecosystems in Amedi district face stress from conflict, fires, climate variability, and unsustainable land use. This study uses remote sensing and geospatial techniques to assess forest degradation from 2000 to 2024. Modified Soil-Adjusted Vegetation Index (MSAVI2), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Moisture Index (NDMI) data are used to measure changes in forest land (FL) and non-forested land (NFL), NDMI, and LST. The results obtained from NDVI data reveal inadequate regeneration rates to counterbalance the total forest loss, while non-forest regions have significantly increased from 5.8% (160.8 km²) to 19.7% (546.6 km²). NDVI-based FL dropped from 90.3% (2505.3 km²) to 73.3% (2032.2 km²), and MSAVI2-based FL declined from 86.3% (2393.3 km²) to 74.3% (2062.3 km²). Conversely, NDVI also indicated an increase in degraded land cover, especially during 2013–2015 and 2020–2024. The LST class (<35°C) declined from 849 km² to 520 km², while the High (45–50°C) and Extreme (>50°C) LST zones exhibited a marked expansion. Mann-Kendall tests revealed significant temporal changes in NDVI, MSAVI2, NDMI, and LST (Z > ±1.96). Sen’s slope quantified trends; Pearson's correlation confirmed strong vegetation/climate stress links (r > 0.7, p < 0.05). The Mann-Kendall trend test confirmed a significant upward LST trend (τ = 0.312, p = 0.034), with a Sen’s slope of 14.662 km² per unit time. Trend detection using the non-parametric Mann-Kendall test validated significant changes across variables without assuming normality. The ARIMA (1,0,0) model forecasts a continued decline through the projection horizon. Forecast intervals widen at a 95% confidence level due to uncertainty in a near-unit root process. The forecast error measured by MAPE is about 5.0%, showing moderate reliability. ARIMA models further revealed robust forecasting capability for environmental indicators, validating model assumptions with stationary residuals and minimal error. For NDVI-derived forest area, a negative trend was identified (Sen’s slope = –22 km²/unit time, p < 0.0001), confirming ongoing deforestation. Pearson correlation analysis showed strong associations between vegetation indices and temperature/moisture variables (r > 0.7, p < 0.05). Although robust, these relationships may be influenced by confounding factors such as climate change, forest degradation, forest fires, armed conflict, and broader land use and land cover changes. Findings emphasize conservation strategies to combat deforestation, warming trends, and safeguard ecosystem resilience. Continued decline through the projection horizon. Forecast intervals widen at a 95% confidence level due to uncertainty in a near-unit root process. The forecast error measured by MAPE is

Evaluating the impact of adding distillers' dried grains with solubles (DDGS) on broiler performance was the aim of the current experiment. A total of 240 one-day-old broiler chicks (Ross 308) were obtained from a local hatchery and divided into four dietary treatments randomly. There were four replicates of each treatment, fifteen birds per replicate. In broiler diets, DDGS was added at percentages of 0%, 10%, 20%, and 30% DDGS. At day 42, one bird per replication was selected to examine blood biochemistry, carcass traits, and meat quality. The findings showed that the group of birds fed a 20% DDGS considerably (P<0.05) improved the growth performance and feed conversion ratio (FCR) compared to the other groups. In addition, the birds fed DDGS basal diets were considerably (P<0.05) lower in total cholesterol, triglycerides and LDL compared to the control. The inclusion of 30% DDGS led to decreased broiler performance and carcass yield. In conclusion, the DDGS can be fed to the broilers up to 20% without adverse effects on growth performance.

This study evaluated the effects of strain and dietary supplementation with Telfairia occidentalis leaves (TOL) on growth performance, blood profile, immune indices, caeca microbial counts, and carcass traits of broilers raised under tropical conditions. A total of 288 male one-day-old chicks (144 Arbor Acres [AA] and 144 Cobb 500) were randomly assigned to 24 replicates (six birds per replicate) and allocated to four dietary treatments: negative control (NC, basal diet without supplementation), positive control (PC, basal diet + 0.4 g/kg oxytetracycline), basal diet + 2 g/kg TOL, and basal diet + 4 g/kg TOL for 35 days. The TOL-4 birds (1731 g) exhibited heavier body weight gain than other birds (1542-1549 g) except the PC birds (P<0.05). AA broilers showed higher ether extract digestibility (63.74% vs. 60.33%) and lower fiber digestibility (47.135 vs. 51.89%) than Cobb 500. The PC birds had the highest ether extract digestibility (66.85%), whereas TOL-4 birds had the highest fiber digestibility (53.30%) (P<0.05). TOL-2 and TOL-4 diets reduced Salmonella spp. counts compared to other groups. The PC birds had lower granulocyte concentration. AA broilers exhibited higher granulocyte, mean corpuscular hemoglobin, and platelet counts and lower IL-1β than Cobb 500. White blood cells were lower in TOL-2 and TOL-4 groups. NC birds had lower immunoglobulins and higher IL-1β than other birds. A significant strain-diet interaction was observed for serum lipids, TNF-α, and IL-10. Dietary supplementation with TOL-4 improved growth performance and fiber digestibility in broilers. However, the impact of TOL on cecal microbiota, and selected serum lipids, blood profile, and immune indices was strain-dependent.

Water is the primary component of the earth, essential for all known forms of life. It is a critical nutrient for maintaining homeostasis in the human body and exists in three states: liquid, solid, and gas. Ensuring drinking water remains clean, safe, and reliable is vital. Factors such as water contamination, heavy rainfall, and agricultural residues which can transfer parasites from soil to the water's surface contribute to the spread of parasitic diseases. This study focused on detecting different parasite stages in various water sources in the Amedi district of Duhok. A total of 250 water samples collected from 6 different sources including 80 samples from household tanks water, 45 samples from sewage water, 40 samples from small rivers canals, 35 samples from ponds, 25 samples from drinking municipal project and 25 samples from springs in Amedi district. For parasitological analysis, all water samples were transported in cold boxes to the Microbiology and Parasitology Laboratory at the Medical Laboratory Department of Amedi Technical Institute. The sediment from each sample was examined under a microscope to identify helminth eggs, trophozoites, and protozoa cysts. Staining was performed using Lugol's iodine and the modified Ziehl-Neelsen acid-fast method. Non-nutritive agar supplemented with Escherichia coli was used for the cultivation of Free-Living ‎Amoebae‎. The rate of contamination of water in current study recorded as higher rate with Giardia lamblia 13,2 % followed by Entamoeba histolytica 11,6 %, while Entamoeba coli and Ascaris lumbricoides recorded 5,6 %, then both Enterobius vermicularis and Cryptosporidium spp. found in rate of 4%, the lowest rate of contamination found by free living amoeba Naegleria fowleri and Acanthamoeba. These findings underscore the urgent need for improved water quality management in the Amedi district. Policy recommendations include the implementation of routine water monitoring programs, stricter regulations on agricultural waste disposal, and investment in modernized water infrastructure to prevent contamination through pipe cracks and leaks. Additionally, community health education campaigns should be promoted to raise awareness about waterborne parasitic infections. Strengthening intersectoral collaboration between public health authorities, environmental agencies, and local governments is critical to ensuring a sustainable and safe water supply.

Human Activity Recognition (HAR) is the main software component in healthcare, sports, and interactive mobile applications. The accuracy of HAR component is strongly tied with the sensor used for the motion detection and it dictates the overall performance of the application. This paper investigates the use of Inertial Measurement Unit (IMU) sensors embedded in smartphones to investigate the HAR accuracy through machine learning approach. The accelerometer and gyroscope outputs are utilized to classify six human activities: going downstairs, going upstairs, sitting, standing, walking, and running, using Recurrent Neural Network (RNN), Random Forest (RF), and Deep Learning (DL) algorithms. A time series dataset comprising XYZ-axis measurements from accelerometer and gyroscope sensors across four types of smartphones, involving 30 participants is used to train the machine learning (ML) models. To enrich the dataset, sensor filtering, and fusion techniques are employed to evaluate different scenarios. The findings of the study provide significant insights into the capabilities of smartphone-embedded IMUs for HAR in mobile applications.

Friction Stir Processing (FSP) is a promising technique to improve the surface properties of materials. Compared to metals, the FSP of polymeric materials was less studied. Therefore, in the present work, high-density polyethylene (HDPE) surface composite was fabricated by the addition of reinforcements such as Graphene (C), Silicon carbide (SiC), Silicon dioxide (SiO2), and Copper (Cu) powders. The effect of FSP parameters such as tool rotational speed (478, 679, and 925) rpm, traverse speed (22, 37, and 51) mm/min, and amount of added particles (10, 15, and 20) % on the mechanical properties of the fabricated samples were studied. The results showed that as the reinforcement powders were added to the matrix material, led to enhancements in each of the tensile strength, microhardness, and impact strength. At the optimum set of process parameters, the graphene-reinforced composites have better improvement in terms of tensile and impact strength where increased by (47.2 and 55.6) % respectively. On the other hand, in terms of microhardness, the SiC-reinforced composite exhibited greater augmentation, registering 20.16 %. While, the elongation of all fabricated composites decreased in comparison to the parent material. Based on the table of analysis of variance (ANOVA), it is noticed that the tool rotational speed has the largest effect on the quality of the products among the other parameters, followed by tool traverse speed and the amount of particles has the least contribution and effect. Considering the information provided, the friction stir process method can be used to repair cracks and other defects in polymeric materials.

Brain tumors, being the most severe and complex kind of cancer, necessitate specialized investigation for diagnosis, treatment, and care. Early recognition of brain tumors enhances patient care and reduces mortality rates. The application of deep learning in MRI diagnostics has transformed medicine. The study employs real and synthetic MRI data to evaluate novel deep-learning models to enhance brain tumor diagnosis. The ensemble model employed AlexNet, VGG16, and ResNet 18 on MR data from Rizgary Hospital in Erbil and Hiwa Hospital in Sulemani, as well as synthetic images produced by Deep Convolutional Generative Adversarial Networks. Modeling measurements encompassed accuracy, precision, recall, and F1 score. The architecture of ResNet18 and its capacity to incorporate residual connections for feature mapping enabled it to surpass all other models in classification accuracy, achieving 99%. Although AlexNet and VGG16 achieve accuracies of 98.16% and 98.83%, respectively, ResNet18 excels in differentiating between normal and unusual instances. DCGANs excel in generating synthetic images and enhancing image categorization and model precision. A study utilizing both real and synthetic images showed that synergistic virtual paradigms could enhance the accuracy of clinical instruments and facilitate deeper AI integration in medicine. Subsequent research will focus on optimizing model architecture and implementing data augmentation techniques to enhance classification accuracy. This Python study demonstrates that deep learning can enhance the diagnosis and treatment of brain tumors.

This study addresses the challenge of balancing heritage conservation with modern environmental performance requirements by developing a Decision Support System (DSS) to aid in selecting the most effective retrofitting strategies for heritage buildings. The lack of a standardized, adaptable framework for optimizing retrofitting solutions highlights the need for a robust assessment tool.

Despite the availability of multiple retrofitting strategies, heritage buildings require a systematic approach to selecting interventions that align with conservation principles while optimizing environmental performance. Current methodologies lack a decision-making framework that integrates heritage significance and environmental impact, while also considering economic feasibility. This study fills this gap by developing a DSS tailored for heritage retrofitting.

To achieve this, the study employed a mixed-methods approach, integrating both qualitative and quantitative research methods to establish a robust DSS framework. The DSS evaluates various retrofitting options based on three essential criteria: heritage preservation, environmental impact, and economic viability. Additionally, expert insights were incorporated through a structured questionnaire, and the EFFESUS (Energy Efficiency for EU Historic Districts’ Sustainability) project methodology was adapted for the scoring and weighting system to enhance the tool’s reliability.

The DSS evaluation identified Liquid Waterproofing Membrane as the most effective solution for moisture management, Aerogel Insulation as the optimal choice for thermal regulation, and Double Glazing with Low-E Coating as the best strategy for energy-efficient window retrofitting. These findings confirm the DSS’s ability to systematically assess retrofitting measures while preserving heritage integrity.

The study is limited to selected retrofitting interventions within the context of Erbil’s heritage buildings, and further validation in diverse heritage settings is required to enhance the DSS’s applicability. This research contributes to the expanding field of sustainable architectural conservation, offering practical insights and a strategic framework for optimizing retrofitting interventions in heritage buildings.

Differential protection is a fundamental protection scheme used to detect and isolate internal faults in power transformers. It works on the principle of comparing the currents entering and leaving the protected zone (transformer). Under normal operating conditions or external fault, the current flowing into the transformer should equal the current flowing out, with taking into consideration the transformer’s turn ratio. Under normal conditions, the differential current between primary and secondary sides should be close to zero, a small difference value may exist due to transformer magnetizing current, current transformer (CT) measurement errors and Tap changer position. The Tap changer position will cause the transformer turn ratio errors which is a cause to give trip signal from differential relay to the transformer circuit breaker if it is not compensating in the differential protection relay. Using the new technique of on-load tap changer (OLTC) position to reduce the turn ratio error in order to improve the differential protection sensitivity, the proposal technique includes new equations that are simulated by PSCAD/EMTDC software. It can be clearly shown that the proposed technique will reduce the turn ratio error to the minimum value with comparing to the conventional schemes.

An important part of hydraulic engineering that is vital for the security and durability of bridge structures understands the velocity of water flow around bridge piers, and this is what this research aims to do. The flow characteristics around three different pier shapes—triangular, rectangular, and circular—were the primary focus of the investigation. The study tested the computational model against experimental data from a reputable source to make sure it was accurate. A discrepancy of less than 10% was found between the model's predictions and the actual experimental data, indicating a good degree of agreement. The results showed that the maximum flow rates just before the center piers varied with pier shape. The highest velocity recorded by the rectangular pier was 0.592 m/s, while the round and triangular piers reached 0.553 and 0.550 m/s, respectively. As one moved farther downstream, the velocity of all designs dropped significantly. At 0.261 m/s, the rectangular pier was the fastest, followed by the triangle at 0.256 m/s, and finally the round at 0.249 m/s. According to the findings, the smoother form of the circular pier is more successful at decreasing flow disturbance than that of the rectangular pier, which has sharper edges and a larger frontal surface and so disrupts the flow more. Findings from this research are crucial for hydraulic engineers because they show how to choose the right pier designs to increase the stability and longevity of bridges without altering the natural flow conditions.