Al-Qadisiyah Journal for Engineering Sciences

In this review article, various cutting-edge strategies are addressed that aim to enhance the mechanical and
functional properties of Biocompatibility metal, which are manufactured using additive manufacturing techniques,
in particular for biomedical applications such as implants and prostheses. Additive manufacturing has several
advantages, including time saving and cost reduction, especially in small product manufacturing processes and
prototypes, freedom of design for complex shapes that are difficult to achieve by traditional methods, the advantage
of reducing waste and material waste, the possibility of customizing products to order, enhancing sustainability and
reducing environmental impact, and others. It can be said that most of the previous studies focused either on the
biological properties of biometallics that were manufactured using addition techniques or improving mechanical
properties, while comprehensive strategies that integrate the two aspects together have not been reviewed, and this
article shows how to achieve synergistic improvements between the two performances. Any modern integrative
revision combines various strategies (alloying, microscopic, surface, computational).

This paper presents a comprehensive study on the structural synthesis and classification of planetary gear mechanisms (PGCMs), which combine the advantages of planetary gear trains (PGTs) and cam mechanisms to produce precise, intermittent, or variable-speed motions. These mechanisms are particularly suitable for high-performance industrial applications, such as indexing tables, path generation systems, and rigid body guidance. The work begins by detailing the kinematic and structural characteristics of PGCMs and identifying possible configurations based on degrees of freedom, link types, and joint arrangements. Using graph theory, rooted graph representations are developed to systematically enumerate two-degree-of-freedom PGCMs, up to seven links. A spanning-tree-based synthesis method is used to generate candidate structures, followed by genetic compatibility analysis to identify viable PGCM configurations. The proposed method produces a set of functionally and structurally distinct PGCMs, including a novel five-link mechanism that achieves precise path generation with minimal complexity. This study not only enhances the theoretical framework for PGCM design, but also provides a practical basis for its application in modern mechanical systems.

This article delves into the intricate dynamics of groundwater flow within duct systems, examining both critical and sub-critical flow conditions. Employing mathematical models, sophisticated potential methodologies, numerical simulations, and flow net analysis, the research investigates the behaviour of the phreatic surface under varying flow coefficients m and slope angles θ. Noteworthy discoveries include the significant influence of the flow coefficient on the curvature and deflection of the phreatic surface, with higher m values resulting in steeper slopes. Additionally, the study emphasizes that changes in slope angle θ impact the interface's shape, leading to variations in flow depth. Innovative visualizations incorporating streamlines and velocity potential contours offer insights into flow patterns, recirculation zones, and potential turbulence areas. These critical findings supply essential insights for enhancing environmental strategies, optimizing water resource management, and improving the efficiency of fluid systems. The study emphasises how important it is to use flow net analysis and thoroughly investigate critical and sub-critical flow scenarios in order to handle issues related to groundwater management and sustainability. Stakeholder can enhance their capacity for fluid system optimization by applying these analytical tools, leading to improved environmental outcomes and informed decision-making.

Keywords

Generally, the production processes of citronella (Cymbopogon Nardus) oil refining in Aceh are traditionally carried out, making them less efficient in resource usage. Every activity along the citronella supply chain has an environmental impact due to raw materials, water, and energy resources. Therefore, this study aimed to assess the environmental impacts caused by activities along the citronella oil supply chain based on a life cycle assessment framework. We compared two production process methods, namely a distillation system with one boiler for one kettle and one boiler for three kettles. The environmental impacts measured include greenhouse gas emissions, global warming potential, land use, and air acidification. Research stages involved determining goal and scope, data collection, environmental impact measurement, interpretation, and proposals for improving the production process. The results showed that the production process of a single boiler system for three kettles had an environmental impact lower than the other method. Furthermore, the distillation process and solid waste combustion have a significant impact on the environment, hence they need to be improved. This life cycle assessment of the citronella oil supply chain is the basis for enhancing the production process to reduce environmental impact.

Climate change, workforce shortages, and sustainability requirements create serious obstacles for oil palm farms, which contribute $65 billion annually. Automated monitoring solutions are very important because traditional manual tree counting techniques, which are widely used in the sector, have counting error rates of 15-25% and require significant human resources. In this comprehensive review, deep learning applications, specifically, convolutional neural networks (CNNs) for oil palm tree detection and counting are evaluated. Performance, constraints, and realistic deployment pathways are examined. Publications from 2016 to 2025 that focused on oil palm recognition using deep learning with quantitative measurements were found using a literature search across Scopus, Web of Science, IEEE Xplore, and Google Scholar. Architectures, dataset properties, and performance metrics were recorded using data extraction. Malaysia is at the forefront of cooperative networks that span 22 nations, according to an analysis of 47 datasets. With mature trees, modern CNN architectures improved with YOLO frameworks achieve >95% detection accuracy; nevertheless, for young trees, they show notable degradation (87.2% vs. 96.8% mAP). Cross-regional generalization (21.9 percentage point accuracy degradation), processing demands (450-650 ms inference), and financial obstacles are important obstacles. Real-time viability is demonstrated by edge-optimized models, which achieve 98.6\% accuracy with 80 ms inference. Geographic bias (68% Malaysian, 23% Indonesian) and restricted public availability (8%) are revealed by dataset analysis. Deep learning can significantly improve oil palm management by 15-20% compared to conventional techniques. Widespread adoption requires standardized benchmark datasets (10,000+ images), transfer learning techniques (<500 images per region), edge-optimized architectures (<100 ms inference), and phased deployment (10-50 hectare pilots). CNN's convergence with precision agriculture positions the industry for comprehensive digitalization while addressing sustainability and labor challenges.

his work explores the use of photoelectrocatalysis (PEC) as one of the most advanced oxidation processes (AOPs) for petroleum refinery wastewater (PRW) treatment. A photoelectrochemical reactor equipped with a Gr/Bi-Ni-Sb-SnO2 photoanode and UVC irradiation was designed to enhance the degradation of pollutants. The effects of key operational parameters, including current density, pH, and anode rotation speed, on the treatment efficiency were systematically evaluated. The results confirmed that increasing the current density provided better chemical oxygen demand (COD) removal, while increasing pH above 5 results in a decrease in COD removal. In addition, increasing the anode rotation speed improved mass transfer and pollutant decomposition up to 200 rpm; after that, no significant improvement was observed, with a slight decrease in COD removal. According to the results, the best performance was achieved at a pH of 5, a current density of 6 mA/cm2, and a rotation speed of 200 rpm, achieving a COD removal efficiency of 86.2% in 90 min with an energy consumption of 110.34 kWh/kg-1 COD. Kinetic studies confirmed that the degradation of COD over time exhibited pseudo-first-order kinetics, with an R2 of at least 0.999. Considering all factors, the results demonstrate that PEC technology, using Gr/Bi-Ni-Sb-SnO2 Photoanode, offers a low-energy, sustainable, and facilitating method for the degradation of complex petroleum-derived pollutants. This study highlights the potential of PEC as a practical alternative to conventional industrial wastewater treatment methods.

The current work investigates the application of deep learning methodologies for detecting and classifying tumors in mammograms. A comprehensive analysis was conducted by evaluating various pre-trained neural network architectures to identify the model that delivers optimal performance. Among the tested architectures, Residual Neural Networks and Densely Connected Convolutional Networks were explored extensively. Data preprocessing, including data augmentation, was a critical step due to the limited availability of public medical imaging datasets. This process ensured diversity in the data and improved model robustness. This paper evaluates deep-learning models for mammogram tumor detection. Experiments without the AdamW optimizer and horizontal flip showed overfitting and low precision (below 40\%). Densely Connected Convolutional Networks achieved high precision but exhibited overfitting with noisy validation loss curves. Adding AdamW and horizontal flip reduced overfitting but lowered overall performance. In classification, the model detected tumors in 9 out of 16 images, showing potential but requiring improvement. The model's ability to detect calcification tumors enhances robustness. YOLO network metrics were modest, reflecting the task's complexity, but results were acceptable for tumor classification challenges.

AR technique is one of the innovative techniques of our time, which is increasingly used in the field of architecture at several levels and for multiple purposes. One of these is to enhance public participation in architectural design in an easy and understandable way. Due to limited attention to this emerging technique in our local context, including the academic one in Iraqi universities, and the scarcity of research contributions addressing it, this paper explores the concept of AR, its utilizations in architecture, and its role in promoting public participation in design. Also, it involved developing a mobile AR app " BUMAR " and testing it in real-world settings, all with the goal of introducing this technology and exploring its potential to facilitate and achieve public participation in design. To achieve this, a proposed virtual model was created as a hypothetical building for the Petroleum Engineering Department, intended to be built. The model was exported to the app, which was shared on social media for the target audiences. The app was tested in displaying and evaluating the model, with experiments conducted over several days by students, faculty, and others. This was accompanied by a questionnaire to gather opinions on BUMAR 's effectiveness, specifically, and the importance of the AR technique in achieving understanding and interaction with the proposed design and its role in facilitating participation and expressing opinions. BUMAR achieved good results, as indicated by the questionnaire results showing acceptance, satisfaction, interaction, interest, requests for further development of the app, and willingness to participate in future augmented experiments. This supports the claim of the importance of AR technique and the success of BUMAR in explaining it to users, suggesting further development of the app and its use in evaluating real construction projects in the future.

Keywords

Assessment of bone age, which represents the development and maturity of bones. It helps treat various pediatric conditions and address legal issues. Conventional bone age assessment is a complex and laborious procedure that is susceptible to inconsistencies between different reviewers and within the same reviewer. Artificial intelligence is a new automatic, accurate, and fast method used to evaluate bone age from X-ray images. In this work, a new network design based on AI methods is proposed. This network is based on the dataset obtained from the Paediatric Bone Age Challenge organized by the Radiological Society of North America. The collection comprises 12,600 radiological pictures of left hands, each labeled with the patient's bone age and sex. The design involves two steps: first, using a Faster R-CNN mask by training the ResNet50 model to select regions of interest and then entering the selected regions into three models (Inception v3, GoogleNet, and ResNet50) for regression-based bone age estimation. The results from these models vary based on their internal structure. ResNet50 yields a mean absolute error (MAE) of 10.6 for males and 9.5 for females. Inception v3 has an MAE of 7.5 and 8.3 for Males and females, and GoogleNet has an MAE of 8.4 for males and 9.2 for females. These models can enhance the precision and effectiveness of bone age prediction.

The current study's objective is to use Diesel-RK simulation software to numerically assess the effects of employing waste plastic oil blends on the thermal characteristics of diesel engines. Each autonomous zone's governing equations are solved using the multi-zone combustion model. Six distinct volumetric mixes were used to analyze the engine's characteristics of waste plastic oil (10%, 20%, 30%, 50%, 70% and 100%) as a comparison to the standard diesel case. The data collected showed a slight decrease in pressure and heat release for all waste plastic oil blends compared to pure diesel fuel. The Sauter mean diameter of droplets increased by 0.91%, 1.85%, 2.68%, 4.35%,5.74% and 7.1% for 10%, 20%,30%, 50%, 70%, and 100\% waste plastic oil (WPO), respectively. The slightly lower cetane number of WPO compared to fossil diesel resulted in a longer ignition delay, resulting in a slightly later combustion start. Brake specific fuel consumption (BSFC) increased by 2.2%, 4.1%, 5.86%, 9.23%, 15.14%, and 15.960% for 10%, 20%,30%, 50%, 70%, and 100% WPO, respectively, because of differences in density, viscosity, and heat content, respectively. A significant drop in the Bosch Smoke Number (BSN) was observed with 30% and 50% WPO, reporting reductions by 2.86% and 4.55%, respectively. The lowest increase in particulate matter was 1.96% and 2.87% for 30% and 50% WPO biodiesel blends. A higher biodiesel content resulted in lower NOx emissions compared to diesel. The findings suggest that 30% WPO is the optimal blend recommended for use in a diesel engine without alteration, which aligns with the results from other studies.

This study presents a simulation of electricity generation systems utilizing solar energy, employing TRNSYS and EES software to address the energy needs of isolated areas in Algeria. Two solar energy systems were compared: the CSP-ORC and the Photovoltaic, analyzed across three regions: Adrar, Illizi, and El-Bayadh. The CSP-ORC system was enhanced by incorporating components for optimizing the operation of the pump within the ORC, as well as integrating a solar photovoltaic system with battery storage at a capacity of 50 kW. The estimated electrical power generated by the studied systems is approximately 1 MW. We compared the technical performance of a 1 MW s-ORC system with thermal energy storage against that of a solar photovoltaic system of the same capacity. This analysis underscores the viability of both CSP-ORC and PV systems for electricity generation in isolated arid regions of Algeria. However, the superior performance of the PV system, particularly during the winter months, suggests that while CSP-ORC systems are promising, they may require further enhancements or integrated solutions (such as hybrid systems) to improve output and reliability across all seasons. The economic analysis highlights the cost-effectiveness of PV systems, which have lower investment, maintenance costs, and LCOE than CSP-ORC. The lowest LCOE (0.0311 €/kWh) and fastest payback (3.62 years) were observed in Illizi for PV, while CSP-ORC had the highest LCOE in El-Bayadh. Environmentally, PV reduces 55.2 tons of CO2 emissions in Illizi, whereas CSP-ORC, generating more electricity, prevents 84 tons. Both systems significantly cut emissions compared to diesel generators.

In Oman's hot-arid climate, school buildings often experience elevated indoor temperatures due to inefficient building envelope designs, resulting in a heavy reliance on air conditioning systems and high energy consumption. To address this issue, this study aims to evaluate indoor air temperature in typical Omani school buildings and determine how modifications to key envelope components, such as walls, roofs, and glazing, can improve indoor temperature conditions and enhance thermal comfort. A mixed-method approach was adopted, combining field measurements with computer simulation. Indoor temperature and relative humidity were monitored in selected classrooms, while DesignBuilder software was used to model existing conditions and test alternative envelope configurations. This integrated method is appropriate for quantifying the thermal performance of building envelopes under realistic climatic and operational settings. Results showed that baseline classrooms reached average indoor temperatures of 33 °C in summer and 24.5 °C in winter, exceeding recommended comfort limits during hot periods. The most effective design alternative reduced indoor temperature by up to 2.3 °C and increased the number of hours within the comfort range by 29%. The study concludes that optimizing wall insulation, roof composition, and glazing selection can significantly improve indoor thermal comfort and energy efficiency in Omani schools, providing practical design guidance for future educational buildings in hot-arid climates.

n today's complex and rapidly evolving world, organizations are increasingly confronted with a diverse workforce in terms of culture, language, generation, religion, gender, and ethnicity. While such diversity presents certain challenges, it also offers valuable opportunities to enhance organizational performance. The present study focuses on the construction industry in Iraq and aims to analyze the impact of HRDM on its consequences, with particular emphasis on the mediating role of HRMD strategies. This research adopts an applied and sequential exploratory mixed-methods approach (qualitative–quantitative). In the qualitative phase, key themes were identified through interviews and thematic analysis using MAXQDA software, leading to the development of an initial conceptual model. This model was then tested in the quantitative phase using a structured questionnaire and data analysis through SmartPLS, applying structural equation modeling (SEM). The findings revealed that HRMD strategies serve as significant mediators between HRMD practices and their consequences, and that transformational approaches can foster improved performance and team cohesion. The proposed conceptual model offers a strategic framework for policymaking and implementation of HRMD programs in the construction sector and similar industries across developing countries.

The sixth generation (6G) networks represent the revolutionary processes in the field of wireless networks, such as ultra-dense network (UDN) frameworks, multi-dimensional connectivity, and network automation procured by AI. Nevertheless, the high rate of small cell and heterogeneous network environment proliferation poses severe challenges in handover management that result in higher signalling overhead, latency, and service interruptions. This review paper investigates the latest handover management solutions in 6G UDNs with some of the most significant challenges being mobility prediction, resource, and security constraints. We especially examine the new solutions, such as machine learning (ML)-based mobility prediction models, Long short-term memory (LSTM) and gated recurrent unit (GRU), reinforcement learning (RL)-based handover decision models, and split federated learning (SFL) of privacy-preserving optimization. Moreover, we will look at network-slicing integration and blockchain-based security solutions as an effort to ensure an efficient and dynamic handover procedure. The paper gives a methodological future study roadmap to optimisation of handover in ultra-dense 6G networks, which synthesizes existing approaches with research gaps identified. These results point to the necessity to optimise the interactions between layers and coordinate network efforts by using artificial intelligence and the proactive handover paradigm to provide seamless, low-latency, and energy-efficient mobility management in future next-generation wireless networks.

This work examines the two-dimensional continuous flow of a Casson-Williamson fluid over a stretched surface under a Darcy-Forchheimer permeable medium. Several elements can affect the flow, including Joule heating, radiation, chemical reactions, thermal sources, electric field influences, and magnetic field influences. Nonlinear partial differential equations articulate the fundamental equations governing the system's dynamics in this physical model. We simplify these equations to a system of nonlinear ordinary differential equations by applying requisite changes. The Keller Box technique is utilized to simplify this collection of ordinary differential equations. Velocity, temperature and concentration graphs are plotted. The velocity profiles decline with a rise in the Casson parameter, magnetic parameter, porous parameter, Weissenberg number, and velocity slip parameter. Still, the electric field parameter diminishes when the speed slip constraint is enhanced. This study primarily examines several local properties, including the skin resistance coefficient, the Nusselt number, and the Sherwood numbers. We compare our results with the current literature by computing the skin friction coefficient for different inputs of the Casson factor. Prior studies have yielded results that are fairly congruent with this one.