Al-Bahir Journal for Engineering and Pure Sciences

This paper presents an Enhanced Cuckoo Search Algorithm (ECSA) to optimally place and size Distributed Generation (DG) in radial distribution systems to minimize real power loss within operating constraints. The proposed ECSA has exponentially decaying adaptive Lévy flights, constraint-aware solution repair with dynamic penalty coefficients, and diversity-directed stochastic replacement to enhance search robustness and convergence speed. It was tested with 30 independent runs on the IEEE 33-bus, IEEE 69-bus, and a practical Nigerian 32-bus distribution network. The simulations show that the ECSA lowers the active power loss of the IEEE 33-bus system from 201.58 kW to 102.75 kW (49.03%), and the IEEE 69-bus system from 224.60 kW to 81.59 kW (61.67%), both statistically significant at p> 0.01. For the 32-bus network of Imalefalafia, losses were reduced from 95.07 kW to 14.78 kW (84.45%), and the minimum voltage was increased from 0.9524 p.u. to 0.9821 p.u. For all test systems, the proposed ECSA consistently outperformed fifteen benchmark metaheuristic algorithms in convergence speed, solution quality, and strength, making it useful for DG planning applications.

Female infertility occurs for many reasons, some unknown. Exposure to heavy metals is a risk factor that negatively impacts on female reproductive system. Therefore, this study aimed to evaluate the effect of cadmium Cd and manganese Mn on luteinizing hormone LH levels for females’ infertility. This study included a healthy group (n = 20), primary infertile females (n = 40), and secondary infertile females (n = 20) collected from Al-Zahraa hospital in Najaf center, Iraq. The Atomic Absorption Spectroscopy (AAS) was used to measure the cadmium and manganese concentrations and LH levels were measured by a Maglumi 800 from Snibe (CLIA). For healthy females, Cd, Mn, and LH concentrations were 0.1307 ppm, 0.00049 ppm, and 6.769 IU/L, respectively. For females with primary and secondary infertility, high concentrations of cadmium, 0.2453 ppm and 0.2296 ppm, respectively, while, the concentrations of manganese were 0.000165 ppm and 0.000307 ppm, respectively. LH levels increased for infertile females (primary and secondary) at 14.2018 IU/L and 15.122 IU/L, respectively. Cadmium and manganese concentrations were indicator to understand why some females unexplained infertility. From the result, the higher concentration of cadmium can act as endocrine disruptors, interfering with the communication between the brain and ovaries. This disruption has been linked to higher levels of luteinizing hormone (LH) and decreased egg quality. The lower concentrations of manganese hinder the body's ability to regulate the pituitary gland, resulting in hormonal imbalances and elevated LH levels. Many women who experience unexplained infertility have underlying hormonal imbalances. The combination of high cadmium levels and low manganese levels creates a compounded effect: cadmium increases oxidative stress and disrupts the function of the pituitary gland, while low manganese limits the body’s ability to combat this stress or manage the resulting hormonal fluctuations.

Accurate forecasting of climate temperature at the regional scale is crucial to agriculture planning, water resource management, and disaster preparedness. Still, due to the nonlinear and complicated temporal patterns in regions such as Iraq, conventional climate models fail to meet this imperative. This paper presents a novel deep learning model that not only predicts monthly average temperature values but also predicts the temperature trend, hence addressing the regional climate prediction problem holistically. The proposed model is designed as a multi-task deep learning framework integrating Convolutional Neural Networks, Long Short-Term Memory networks, and an attention mechanism to process historical monthly temperature data obtained from the Berkeley Earth dataset using a sliding window method. The model is learned a shared representation for both regression and classification, therefore optimized together. Classification accuracy of 94.81%, ROC AUC of 0.9839, and R² score 0.9773 for regression were obtained, thus proving the capability of model to efficiently capture local and long-range temporal dependencies and give importance to influential time steps. As such, the proposed CNN-LSTM-Attention model in multi-task learning configuration presents a promisingly accurate, interpretable, and computationally-effective temperature prediction and trend classification, applicable to regional climate modeling and decision support for climate-sensitive areas.

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO Cluster in ArcMap. The comparative analysis of these algorithms allowed development of four significant land cover classes: built-up areas, vegetation, water bodies, and bare land. The results of the classification for the Mosul study area also indicated that using X-means gave an accuracy of 76.34, while using ISO Cluster gave 73.61, and K-means gave 80.14. The highest classification accuracy was achieved by the use of K-means, suggesting that it is the best algorithm to use in this region

Bismuth oxide (Bi₂O₃) nanoparticles were synthesized via a green route and integrated into Bi₂O₃/Si heterojunction solar cells. Structural analysis confirmed the monoclinic α-Bi₂O₃ phase with a nanoscale crystallite size, while SEM revealed a uniform morphology. Optical studies showed a direct band gap suitable for absorbing visible light. Photoluminescence spectra exhibited dual emissions at 498 nm and 250 nm, attributed to defect states and quantum confinement, highlighting the dual role of Bi₂O₃ as both absorber and transparent window layer. Electrical characterization under dark conditions demonstrated rectifying diode behavior, confirming the formation of a junction. Capacitance–voltage analysis yielded a built-in potential of 0.71 V and a depletion width of 3.66 µm, indicating efficient charge separation. The current-voltage results under illumination indicated an open-circuit voltage of 180 mV, a short-circuit current density of 3.5 µA/cm², a fill factor of 14.4%, and an efficiency of 0.91%. These results confirm that the interface engineering improves photovoltaic cell performance. They highlight the significant benefits of using environmentally friendly Bi₂O₃/Si heterojunctions.

Pseudomonas aeruginosa is a type of bacteria that is found commonly in the environment, like in soil and in water. This bacteria can cause infections in the blood, lungs (pneumonia), or other parts of the body after surgery. These bacteria are constantly finding new ways to avoid the effects of the antibiotics used to treat the infections they cause. Zinc oxide nanoparticles' (ZnO-NPs') or Nano zinc oxide antibacterial properties have drawn a lot of attention from around the world, especially since nanotechnology has been used to create particles in the nanometer range. Because of their larger specific surface area and improved particle surface reactivity as a result of their smaller particle size, ZnO-NPs have appealing antibacterial qualities. ZnO is a bio-safe substance that affects chemical and biological species through photo-oxidizing and photocatalysis. The Aim of study is to evaluate the antibacterial activity and antibiofilm of Zinc Oxide Nanoparticles (ZnO NPs) against many bacteria isolated from burn infection include P. aeruginosa and S. aureus and Klebsiella. The tests in this study focusing on antibacterial and antibiofilm activity of zinc oxide nanoparticles. The results showed a moderate antibacterial activity by nano zinc oxide where 40% of P. aeruginosa isolates as inhibited, Klebsiella was unaffected, and S. aureus was inhibited. On the other hand, it was exhibit a good antibiofilm activity produced by Pseudomonas aeruginosa and Klebsiella, so the nano zinc oxide shows promise as a potent antibiofilm agent

Induction machines serve as the cornerstone and driving force of modern manufacturing and production systems. This research aims to monitor and analyse the operating performance of induction motors using motor current signature analysis (MCSA). MCSA is employed to process the current signal of a motor into a frequency spectrum, known as the current signature by applying the Fast Fourier Transform (FFT) algorithm. The underlying principle is that vibration generated in a motor is closely related to the changes of the magnetic field density, and the induced voltage varies with the stator current.

A simulation model replicating the behaviour of an induction motor was developed and tested under various operating conditions. Advance techniques were used for data acquisition and analysis. The results obtained from both MATLAB/SIMULINK model and the experimental setup reveal that eccentricity faults in induction motors lead to increased current draw, resulting in transient instability, non-uniform acceleration and pulsating torque due to harmonic distortion.

This study offers a comparative assessment of Perturb and Observe (P&O) and Cuckoo Search (CS) maximum power point tracking (MPPT) methodologies, specifically within a photovoltaic (PV) system, under both standard test conditions (STC) and varying irradiance scenarios. The PV system was simulated and modeled using MATLAB/Simulink, and performance metrics were derived from the time-domain simulation outcomes. In the context of STC, the CS MPPT technique demonstrates an output power of 11.62 kW, coupled with an efficiency of 93.46%. This result represents a substantial improvement over the P&O MPPT method, which yields 8.088 kW at an efficiency of 65.05%. In a similar vein, the CS-based methodology exhibits robust performance across a spectrum of irradiance levels, generating 11.51 kW at an efficiency of 92.58%. Conversely, the P&O method produces 8.05 kW with a 64.75% efficiency. These findings indicate that the CS MPPT technique facilitates enhanced power extraction and superior efficiency relative to the traditional P&O algorithm, irrespective of whether the operating conditions are stable or fluctuating. Consequently, it presents a more advantageous solution for photovoltaic energy conversion systems.

Grape (Vitis vinifera L.) is one of the most economically significant fruit crops which has a large adaptive range, nutrition and medicine effect. A variety of 70 types of grapes are planted on more than 11.6 million plants in Iraq, generating a total annual output of more than 421,000 tons. Increasing grape quality, and particularly the phenolic composition recently has been of major interest. Leaf nutrition – particularly seaweed extracts, being rich in amino acids, trace elements and growth promoting substances – has proved to be a potential tool to enhance plant growth and fruit biochemical quality. The effect of grape variety and fertilizer on the phenolic content in grape fruits (Halwani and Kamali) was investigated using the foliar-applied seaweed extract, Agrimax® at 0, 15 and 30 ml/L were applied. Factorial experiment was carried out in 2024 with completely randomized design by three replications. The first foliar treatment was performed on the first day of April and thereafter five sprays were given at 15 days intervals. HPLC was used to determine the amount of phenolic compounds, such as caffeic, gallic, cinnamic, chlorogenic and p-coumaric acid), The data was analyzed using GenStat with LSD at 5% and two-way ANOVA followed by Šidák test. High phenolic traits were found to be significantly different in all, but caffeic, gallic, and cinnamic acids where the Halwani population extracted with methanol outperformed Kamali. Application of seaweed extract (particularly at 30 ml/L) considerably raised contents of caffeic, gallic, cinnamic and chlorogenic acids in comparison to control plants. The highest levels of all significantly altered phenolics were observed in Halwani variety at the concentration 30 ml/L Agrimax®. The level of p-coumaric acid did not significantly change. In conclusion, the Halwani cultivar had the highest phenolic content under Babil Germplasm climatological conditions. Application of seaweed extract by foliar method particularly at 30 ml/L significantly increased phenolic content, indicating its potential to be used as an eco-friendly substitute to mineral fertilization.

Recently, plant-mediated green synthesis has emerged as a sustainable, eco-friendly, and cost-effective strategy for synthesizing zinc ferrite (ZnFe₂O₄) nanoparticles (NPs). This review critically examines the advancements in biosynthesizing ZnFe₂O₄ NPs using various plant extracts, including Aloe vera, Hibiscus rosa-sinensis, Aegle marmelos, Eucalyptus leaves, Tragacanth gum, Piper nigrum, and Moringa oleifera, among others. It emphasizes their dual role as reducing, capping, chelating, and stabilizing agents. The article extensively compares physicochemical properties of the green-synthesized NPs in terms of crystallite size, morphology, magnetic behavior, and band gap energy. Achieved through different synthesis routes such as sol-gel, hydrothermal, co-precipitation, and microwave-assisted methods. Special attention is given to the roles of phytochemicals, reaction conditions (pH and temperature, etc.), and post-treatment parameters in tuning the properties of NPs. Moreover, the review highlights the multifunctional applications of bio-synthesized ZnFe₂O₄ NPs in photocatalytic degradation of organic dyes, antimicrobial activity against pathogenic strains, anticancer uses, and their integration as anode materials in lithium-ion batteries. Through a structure-function correlation analysis, this study establishes ZnFe₂O₄ NPs as a promising platform for green nanotechnology, with significant implications for environmental remediation and biomedical innovation.