Al-Khwarizmi Engineering Journal
type: channel
description: Al-Khwarizmi Engineering Journal (ALKEJ) is a peer-reviewed, international open-access journal published quarterly by the Al-Khwarizmi College of Engineering at the University of Baghdad, since its establishment in 2005. The journal is dedicated to the publication of original and impactful research across all fields of engineering. These include, but are not limited to: Biomedical, Electrical, Mechanical, Computer, Mechatronics, Biochemical, Information Technology and Communications, as well as Manufacturing and Automation Engineering. ALKEJ aims to serve as a platform for researchers and professionals to share innovative findings and significant advancements that contribute to the global engineering community. Please see our Author Guideline for information on article submission.
publisher: Al-Khwarizmi College of Engineering / University of Baghdad
language: en
publication name: Al-Khwarizmi Engineering Journal
issn: 1818-1171
copyright: Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
items: Real-Time Object Detection in Complex Environments: Leveraging Deep Learning and Computer Vision Techniques
items: Advanced Hybrid Convolutional Neural Network for Leaf-Based Plant Disease Detection
items: Spectral Efficiency and Hybrid Multi-RSU Deployment with Cost Optimisation in Vehicular Ad Hoc Networks
items: Smart Energy Management System Based on Embedded Wireless Communication
items: Performance Evaluation and Optimisation of Spectrum Management in Communication Systems by GA
items: YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
items: AI Applications for Cerebral Palsy: Focused Review on Diagnosis, Motion Analysis and Rehabilitation
items: Dynamic Job Scheduling in Manufacturing Systems using Deep Q-Learning
items: Analysing the Effects of Switching, Conduction and Thermal Losses on IGBT Performance in Boost DC–DC Converters for Photovoltaic Systems
items: Rabbit-256 Optimisation for Secure Blockchain Hashing in IoT-Healthcare Data
items: Vendor Performance Monitoring in Automotive Manufacturing Under Localization Strategy
items: Design of a Floating Curved Photovoltaic Panel with IoT-Enabled Weather Monitoring for Rivers and Lakes Environments
items: Artificial Intelligence-Based Control Strategies for Flow Rate Control of Fluids and Gases in Energy and Process Systems: A Systematic Review
items: Supply Chain Resilience in the Context of Global Production and Logistics
items: Pulse Wave Speed Estimation: A Comparative Study of the PU-Loop Technique versus Tensile and Compliance Testing on Synthetic Elastic Tubes
items: Preparation, Characterization, and Evaluation of Electrospun Copolymer Nanofiber Membranes for Kerosene Removal from Aqueous Solution
items: Effect of Fibre Loadings on the Tensile Properties of PALF/PHBV Composite: A Two- Parameter Weibull Analysis
items: Review of Current Trends, Materials and Challenges in 3D Printing for Biomedical Applications Using Polylactic Acid
items: Sensitivity Analysis of the Integrated CRITIC-TOPSIS Method for Selecting Optimal Natural Fibre–Reinforced Composites for Toe Caps in Safety Footwear
items: Design of a Compact Broadband Metamaterial Absorber with Wide-Angle Reception and Polarization-Insensitive for Electromagnetic Energy Harvesting in Wireless Sensor Network Applications
items: Optimization of Phenol Adsorption Using Activated Carbon Derived from Spent Tea Leaves in a Packed-bed Column
items: Analytical and Mechanical Study of Transtibial Osseo-Integrated Prostheses Implant
items: Thermal Comfort and Internal Conditions Testing of Standard Relief Tents in cold Climates
items: Real-Time Adaptive Traffic Signal Control with YOLOv10 and Image Processing
items: Levofloxacin Adsorption and Release on γ-Mesoporous Alumina Nanoparticles Using Linear Kinetics Equations
items: Synthesis and Characterization of Flash Graphene for Use in Remediation of Contaminants in Iraqi Groundwater Using Batch Adsorption Techniques
items: Impact of the Number of Reflecting Elements and Positioning of the Reconfigurable STAR-Passive-ORIS in MIMO FSO Systems
items: Autonomous Vehicle Steering-based Feedback Linearization and Sliding Mode Control
items: Biodiesel Production from Castor Oil
items: Effect of Heat Treatment on the Microstructure, Impact Toughness and Hardness of High-Chromium White Cast Iron with Added Niobium and Manganese Minerals

Real-Time Object Detection in Complex Environments: Leveraging Deep Learning and Computer Vision Techniques
type: item
description: This research tackles major real-time detection obstacles such as changing lighting effects and object blocks and background elements and different object sizes because such problems occur frequently in autonomous driving and surveillance and smart infrastructure applications. A detection pipeline system with modular functionality enabled processing of images, videos and webcams in real-time and had specific optimizations for each input format. The YOLOv5s model was selected due to its accurate and fast performance characteristics so we deployed it in a cloud-based Google Colab system with GPU acceleration capabilities. Real-world data collection succeeded in quantitative analysis through assessment of inference duration together with frame speed and detection precision along with confidence values while qualitative methods measured box precision and label validity. The system produced exceptional results by processing images and video data within 28 to 35 milliseconds and webcam frames between 1.8 to 2.3 seconds while generating confidence scores between 0.70 and 0.93. Real-time applications benefit from this system because it presents stable detection while being environmentally flexible and practically applicable. YOLOv5 proves robust based on the discovered test results which indicate future potential deployments of intelligent visual monitoring systems across all dynamic environments.
subject: Articles
  topic: section
subject: Real-time object detection; YOLOv5; Deep learning; Computer vision; Google colab; COCO dataset; Live webcam detection
  topic: keywords
creator: Dawood Hussian
creator: Heba Raheem
creator: Maha Altememe
creator: Yan Li
creator: Shahab Abdulla
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 54
ending page: 71
doi: 10.22153/kej.2026.03.001

Advanced Hybrid Convolutional Neural Network for Leaf-Based Plant Disease Detection
type: item
description: Accurate detection and classification of plant diseases are central to sustainable food production and the reduction in crop losses. Conventional identification protocols are often dependent on experts. They are time-consuming and difficult to scale. This paper proposes a novel Advanced Hybrid Convolutional Neural Network (AHCNN) model combining the attention mechanism and simplified convolution mechanism. This model can obtain high accuracy and low computational complexity. It is trained using a high-resolution leaf dataset that includes 1532 leaf images, separated into 1322 training, 150 validation and 60 test samples, with the goal of a classification of specimens into three categories: healthy, powdery and rusty. Its architecture uses squeeze-and-excitation (SE) blocks and a spatial attention mechanism, which, together, can provide enhanced feature extraction and improve the interpretability of models. Results showed that the model achieved a validation accuracy of 98% and a test accuracy of 98.33%. With a parameter number of only 0.4 million, the proposed architecture provides a lightweight solution that performs much better than conventional deep learning frameworks in terms of computational efficiency. These attributes make AHCNN an interesting candidate for real time, drone-based detection of plant diseases as part of precision agriculture systems.
subject: Articles
  topic: section
subject: Plant disease detection; Machine learning; Convolutional neural networks; Squeeze-and-excitation (SE) blocks
  topic: keywords
creator: Sarmad Mahmood
creator: Swash Mohammed
creator: Yahya Khidhir
creator: Tale Saeidi
creator: Sahar Saleh
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 19
ending page: 31
doi: 10.22153/kej.2026.02.002

Spectral Efficiency and Hybrid Multi-RSU Deployment with Cost Optimisation in Vehicular Ad Hoc Networks
type: item
description: Enhanced traffic management and safety can be achieved using decentralised networks such as Vehicular Ad Hoc Networks (VANETs), which allow communication amongst vehicles. These networks support dynamic, real-time communications; however, they face difficulties in terms of scalability, security and network stability. Improved vision efficiency, cost reduction and enhanced connectivity in VANETs can be realised using multi-RSU and clustering methods, revealing real-world benefits. This research seeks to improve communication efficiency in VANETs by addressing issues such as network delay, power usage and communication cost. The goal is to improve network reliability in a dynamic vehicular setup with fixed Roadside Units (RSUs), ensuring constant connectivity and reduced overhead. Spectral Efficiency and Hybrid Multi-RSU Deployment with Cost Optimisation (SEHMR) approach introduces an innovative solution for improving vehicular communication. The system integrates clustering, scheduling and continuous cluster maintenance of vehicles. By analysing the initial population and probability values of vehicles, the Cost Optimisation (DEA) algorithm effectively optimises costs. RSUs are strategically positioned to improve coverage, communication and power efficiency. This approach is evaluated through extensive simulations using NS2 and the SUMO mobility suite, measuring performance through metrics such as efficiency, throughput, packet delivery ratio, data loss, end-to-end delay and overhead. The proposed SEHMR-VANET is compared with basic approaches such as HGAR-VANETS, ROOP-VANET and ISFF-VANETs. Results indicate that SEHMR-VANET achieves higher energy efficiency and lower delay and data loss, revealing its effectiveness in improving traffic communication.
subject: Articles
  topic: section
subject: Vehicular Ad-hoc networks (VANETs); RSU deployment; Cost optimisation; Clustering; DEA algorithm
  topic: keywords
creator: Mustafa Maad Hamdi
creator: Sami Abduljabbar Rashid
creator: Ahmed Adil Nafea
creator: Lukman Audah
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 85
ending page: 98
doi: 10.22153/kej.2026.01.002

Smart Energy Management System Based on Embedded Wireless Communication
type: item
description: A Smart Energy Management System (SEMS), which uses the Internet of Things (IoT), has been created in response to the increasing demand for energy-efficient solutions. A cost-effective and efficient IoT-based SEMS that optimizes energy usage, reduces costs, and improves sustainability is presented in this work. The suggested solution uses smart meters, cloud-based analytics, and inexpensive sensors (total cost is less than US$20) to track and control energy use in real time. Through the use of Machine Learning (ML) algorithms and data-driven decision-making, the system can forecast future consumption trends and provide consumers with relevant data. Energy conservation is achieved through the system’s affordability, which makes it appropriate for residential, commercial, and industrial applications without requiring significant infrastructure investments. The system’s effectiveness in reducing energy waste while maintaining user convenience is demonstrated by experimental results. The considerable potential of IoT-based technologies in creating an economical and sustainable framework for energy management is demonstrated by this study. Furthermore, energy consumption prediction systems based on ML are presented. In addition, a scalable approach for more intelligent and environment-friendly energy management systems in future smart cities is developed using OPNET simulation for about 1000 nodes (smart meters). The findings demonstrated that boosting family classifiers achieved the best accuracy with a 98% prediction rate. When 1000 nodes are simulated, the latency for the proposed system may reach as low as 0.21 ms with approximately 20 bytes/s of network traffic, according to the network simulation results that used OPNET.
subject: Articles
  topic: section
subject: Embedded wireless communication; Smart meters; Smart energy management systems; IoT; Machine learning for energy optimization
  topic: keywords
creator: Rabee Hagem
creator: Mustafa Qassab
creator: Amar Daood
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 74
ending page: 90
doi: 10.22153/kej.2026.12.015

Performance Evaluation and Optimisation of Spectrum Management in Communication Systems by GA
type: item
description: Whilst wireless communication technologies proliferate, putting extra demand on the finite radio frequency spectrum and leading to issues of congestion, underutilisation and interference, this dissertation presents a modern spectrum management model on the binary genetic algorithm (BGA) capable of improving detection accuracy and adaptive spectrum access in cognitive radio networks (CRNs). BGA follows binary encoding to determine optimum weighting factors for secondary users in a CRN scenario with a much faster performance and better reliability than conventional genetic approaches. In the cooperative spectrum-sensing scheme proposed in this paper, multiple secondary users will forward their local sensing outcomes to a fusion centre in which BGA optimisation will fine-tune the weighting coefficients throughout the soft decision fusion mechanism. The algorithm then evolves from one generation to the next through the application of selection, crossover and mutation operations to discover the best configuration. Extensive simulation experiments were conducted to study the effects of the critical genetic parameters of mutation probability, crossover rate and population size on detection capability. The results indicate that the optimised BGA framework can achieve detection probability close to 96%, false alarm rate of 0.1, mutation rate of 0.12 and bit error rate of around 7 × 10⁻⁵ even when the signal-to-noise ratio is extremely low at –15 dB. In addition, the comparative evaluation showed the definite superiority of the proposed algorithm when tested against conventional algorithms, such as energy detection, matched filtering and neural network-based convolutions, when subjected to challenging and noise-prone conditions. The work further affirms the applicability of evolutionary algorithms in enhancing the cognitive intelligence of CRs and presents a scalable solution for spectrum management in existing 5G systems and future 6G frameworks.
subject: Articles
  topic: section
subject: Cognitive Radio; Crossover; Detection Rate; Dynamic Spectrum Access; Genetic Algorithm; Optimisation; Spectrum Sensing
  topic: keywords
creator: Ahmed Thabit
creator: Bassam Abd
creator: Ali Al-Askery
creator: Haider Oleiwi
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 82
ending page: 92
doi: 10.22153/kej.2025.10.002

YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
type: item
description: Efforts to combat the recent spread of infectious diseases require innovative solutions for characterizing their transmission. This study introduces a system for helping people maintain safe distances from one another, thereby decreasing the transmission of infectious diseases in crowded public spaces. Real-time video footage from surveillance cameras was processed by the cutting-edge “You Only Look Once” ver. 8 (YOLOv8) computer vision model, which can detect and segment individuals in images. YOLOv8 was combined with an embedded system that includes an Atmel 8-bit AVR microcontroller and an Arduino Uno board, a buzzer, and an LCD. This proposed system can generate alerts when people breach the set social distancing limit of 0.5 m. Its new network design increases human detection to an accuracy range of 97%–98%. This proposed system was trained on 70% of the dataset, and validation and testing were performed on 15% and 15% of the dataset, respectively. Combining deep learning with embedded systems creates an intelligent vision-based monitoring system for crowded spaces, addressing key issues pertaining to disease transmission reduction and public health protection. The proposed system can be implemented at a low cost, such as by simply using a resource-constrained embedded device or a microcontroller. This approach overcomes the functional challenge of utilizing artificial intelligence-based surveillance in a scalable, decentralized, and economical manner. In general, the proposed system has a high frame per second rate, which is satisfactory for real-time operation on edge hardware. The novelty of this method relies on the use of the YOLOv8 model to achieve precise performance while balancing accuracy and speed on edge/embedded devices for practical, real-world epidemic control.
subject: Articles
  topic: section
subject: YOLOv8 algorithm; Deep learning; Social distancing; Object detection; Computer vision
  topic: keywords
creator: Khansaa Aljafaar
creator: Zinah Ameen
creator: Rana Abdu-aljabar
creator: Russul Al-anni
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 44
ending page: 54
doi: 10.22153/kej.2026.12.013

AI Applications for Cerebral Palsy: Focused Review on Diagnosis, Motion Analysis and Rehabilitation
type: item
description: Early diagnosis and effective rehabilitation of cerebral palsy (CP) are essential for improving functional outcomes and reducing long-term complications. CP affects the ability to perform daily physical activities and is often associated with secondary health problems such as obesity and chronic pain. Owing to the complexity of the condition, rehabilitation typically requires continuous supervision by clinical specialists, which can be challenging in environments with limited resources. Recent advances in artificial intelligence (AI) have created new opportunities for enhancing CP healthcare. This review summarises the major applications of AI in diagnosis, clinical decision support, motion classification and rehabilitation systems. AI-based diagnostic tools—including medical-image analysis using MRI and CT—support the early detection of neural abnormalities. Motion-classification systems use physical activity data, functional motor scales and gait analysis features to detect deviations and evaluate treatment progress. In rehabilitation, AI is increasingly integrated into robotic systems, virtual reality environments, video game-based training and metaverse-based therapies, enabling adaptive and engaging therapeutic experiences. This study highlights the shift towards AI-enhanced interventions, discusses the importance of human–computer interfaces for improving interaction between patients and rehabilitation systems and outlines current limitations and challenges. Continued research is required to improve data quality, model generalisability and clinical integration of AI technologies in CP care.
subject: Articles
  topic: section
subject: Cerebral palsy; Artificial intelligence; Machine learning; Motion analysis; Rehabilitation systems; Clinical decision support
  topic: keywords
creator: Auns Al-Neami
creator: Moneer Faraj
creator: Lama Bou Farah
creator: Lina Bachache
creator: Rasha Massoud
creator: Rufaida Hussain
creator: Saleh Massoud
creator: Mhd Al Hinnawi
creator: Ahmed Hussein
creator: Samer Alabed
creator: Sami Bennour
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 1
ending page: 18
doi: 10.22153/kej.2026.02.001

Dynamic Job Scheduling in Manufacturing Systems using Deep Q-Learning
type: item
description: In this connection, this paper proposes a Deep Q-Network (DQN) approach to address the dynamic nature of the job-shop scheduling problem. Dynamic scheduling requires an efficient and reliable algorithm for handling disruptions such as machine breakdowns and job priority changes. When comparing DQN with traditional approaches such as genetic algorithm (GA) and PSO, the latter algorithms cannot cope with the problems. On the contrary, DQN gains knowledge from its experiences within the factory and develops a strategy for solving the scheduling problem through minimizing makespan and maximizing machine utilization. Moreover, using experience replay (ER) and target networks enables DQN to maintain stability and develop an optimal schedule. Empirically, it was found that DQN reduces makespan by 24.8% with machine utilization being 92%. From the results obtained, it can be noted that the learning parameters play a great role in determining the performance of the model. Thus, this study proves that DQN is an effective approach for addressing the issue under discussion and could also be used for developing other approaches such as multi-agent and double DQN.
subject: Articles
  topic: section
subject: Dynamic job-shop scheduling; Deep Q-Learning; Reinforcement learning; Machine utilisation; Makespan optimisation
  topic: keywords
creator: Hind Khalid
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 72
ending page: 84
doi: 10.22153/kej.2026.10.001

Analysing the Effects of Switching, Conduction and Thermal Losses on IGBT Performance in Boost DC–DC Converters for Photovoltaic Systems
type: item
description: This study examines the thermal and electrical characteristics of insulated gate bipolar transistors (IGBTs) in boost direct current (DC)–DC converters used in photovoltaic systems with respect to how switching and conduction losses vary under different operating conditions. The behaviour of IKWH70N65WR6 IGBT was measured (PLECS software) with load resistances of 5, 10 and 20 Ω at switching frequencies of 1–100 kHz. Important results are as follows: conduction losses prevail at low frequencies and load currents and switching losses increase towards high frequencies. Thermal stress is the highest at mid-range frequencies (50–60 kHz) and IGBT junction temperatures peak to 123 °C. Reduced load resistance leads to increased power consumption and total losses, and the value of load matching must be optimised. This study offers critical data regarding the choice and optimisation of IGBTs to improve efficiency and reliability within the uses of renewable energies.
subject: Articles
  topic: section
subject: IGBT losses; step-up DC–DC converter; photovoltaic systems; conduction losses; switching losses; thermal control; efficiency optimisation; continuous conduction mode (CCM)
  topic: keywords
creator: Muhanad Almawlawe
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 93
ending page: 105
doi: 10.22153/kej.2025.10.003

Rabbit-256 Optimisation for Secure Blockchain Hashing in IoT-Healthcare Data
type: item
description: The recent trend towards the use of a blockchain as a means to guarantee the security of health data has raised concerns with regard to its applicability in Internet of Things (IoT) scenarios due to computationally heavy primitives (e.g. hashing functions) and lack of scalability. As a solution to this problem, this article introduces Rabbit-256: an addition–rotation–XOR-based sponge construction derived from the Rabbit stream cipher that is twisted and adapted to a lightweight hash function, suitably adapted for distributed solutions in healthcare systems with a blockchain nature. Rabbit-256 is a lightweight encryption cipher that wears the mask of a hash function but with better diffusion and avalanche through an official buildup in Merkle trees. The presented system is evaluated using common cryptographic measures against SHA-256, i.e. grid operators of 100, 500, and 1000 inputs for the avalanche effect, Hamming distance, and mean standard deviation. We observe that Rabbit-256 exhibits a higher security margin and lower computational overhead, and thus, it is an optimal alternative to resource-constrained IoT systems given its resistance against attacks. Although the current work is developed in simulation, Rabbit-256 can be utilised for actual deployment to ensure the privacy of e-health records and medical sensor data in IoT and clinical services over a blockchain. In the future, we will focus on hardware design, energy efficiency, and integration (i.e. to be compliant with the Health Insurance Portability and Accountability Act in the U.S. and the General Data Protection Regulation in Europe).
subject: Articles
  topic: section
subject: healthcare; hashing; medical internet of things; security; cloud computing; blockchain
  topic: keywords
creator: Khalid Jadaa
creator: Aymen Badr
creator: Waleed Hussein
creator: Latifah Kamarudin
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 45
ending page: 64
doi: 10.22153/kej.2025.10.001

Vendor Performance Monitoring in Automotive Manufacturing Under Localization Strategy
type: item
description: Given the competitive nature of the automotive industry, supplier performance must be analyzed and monitored to ensure continuous production while minimizing costs and maximizing quality and service. This research seeks to analyze the practices of vendor performance monitoring by non-Malaysian companies in localization strategies that align with national automotive policies. A case study was performed in ABC Company, a well-established Japanese vehicle manufacturer in Malaysia, through three main research approaches, namely, semistructured interview, site visit, and documentation review. The interview centered on their top management to understand how they monitor supplier performance and determine the current issues they face, while the site visit and documentation review focused on actual practices. A vendor known as WHN Company was chosen for additional analysis in the case study. Result reveals the poor performance of vendors with several root causes such as material shortages, manpower limitations, machine capacity issues, and financial constraints. A cause and effect analysis was performed to deeply understand the root causes. ABC terminates the existing vendors that have been classed in “C” category for 3 months and treated as underperformers. ABC then identifies and develops new qualified vendors to replace them. This case illustrates the importance of vendor monitoring, thorough assessment, and development for optimal performance. It also evaluates the importance of competitiveness and localization of the automotive sector’s strategies on supply chain resilience for sustaining adaptive monitoring mechanisms.
subject: Articles
  topic: section
subject: Vendor performance; Automotive manufacturing; Supply chain management; Localization strategy; Supplier evaluation
  topic: keywords
creator: Wan Hasrulnizzam Wan Mahmood
creator: Mohd Kasim
creator: Mohd Azmi
creator: Hadi Salaam
creator: Mirasukma Razak
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 112
ending page: 126
doi: 10.22153/kej.2026.12.017

Design of a Floating Curved Photovoltaic Panel with IoT-Enabled Weather Monitoring for Rivers and Lakes Environments
type: item
description: Floating photovoltaic (PV) systems enable solar energy generation on water bodies, alleviating land scarcity whilst providing potential cooling and reflection benefits that enhance panel performance. However, many floating installations employ flat PV modules without optical concentration or integrated tracking, which limits their energy yield in tropical environments such as rivers and lakes. This study designs a floating solar platform that incorporates a curved PV configuration mounted on a buoyant platform, a single-axis sun-tracking mechanism and an IoT-based weather and performance monitoring system designed for riverine deployment. This prototype employs an 18 V, 250 W monocrystalline flexible solar panel formed into a curved trough that is controlled by an Arduino-based tracking system and a NodeMCU-based monitoring subsystem to record voltage, temperature and humidity via a mobile application. Experimental testing on the Damansara River indicates that the floating curved system achieves a maximum power output of 231±5 W, which is higher compared with the output of a flat-land-based configuration (190±7), corresponding to an efficiency enhancement of approximately 21% at a solar radiation intensity of 1200 W/m². The floating configuration also exhibits reduced efficiency degradation with increasing ambient temperature due to the cooling effect of the water body and elevated humidity, which together mitigate thermally induced performance losses. These findings demonstrate that the proposed curved floating solar platform provides a feasible and environmentally friendly solution for increasing PV energy yield whilst conserving land resources in tropical regions.
subject: Articles
  topic: section
subject: Curved solar; Floating platform; Sun tracker; Solar; Solar monitoring system
  topic: keywords
creator: Erold J. Baeren
creator: Safaa Najah Saud Al-Humairi
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 99
ending page: 115
doi: 10.22153/kej.2026.03.002

Artificial Intelligence-Based Control Strategies for Flow Rate Control of Fluids and Gases in Energy and Process Systems: A Systematic Review
type: item
description: Artificial Intelligence (AI)-driven controllers have increased the accuracy, flexibility, and efficiency in flow rate control for gas and liquid flows. In contrast, traditional Proportional–Integral–Derivative (PID) controllers are usually not well trained for nonlinear dynamics and time-varying disturbances, leading to limited stability and control accuracy. Most existing research studies have focused on the design and performance of individual AI-based controllers for individual machines without systematically comparing these devices for various industrial and energy applications. The gap of this study is filled by a review of 34 peer-reviewed studies from 2021 to 2024 detected in Scopus and Web of Science databases, classified on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) structure. From the quantitative results the AI controllers have a higher performance overall than traditional PID controllers with faster response by 12 to 85%, 15 to 67% reduction in steady-state errors, and 18 to 40% drop in overshoot, which indicates high accuracy and stability of systems. The data indicates that fuzzy and hybrid controllers are highly flexible for dealing with nonlinear and dynamic flow phenomena while model predictive and optimization-based controllers have high accuracy for multivariable processes. Furthermore, AI control technology for energy, hydrogen, and process fluid applications improves operability, reduces energy overhead, and enables real-time flexible management under ever-changing loads. Thus, this review lays the excellent groundwork for next-generation intelligent control frameworks and opens the new paradigm of advanced and data-driven strategies towards subsequent flow regulation and energy system applications.
subject: Articles
  topic: section
subject: Control system; Flow rate; Adaptive control; Fluid; Artificial intelligence
  topic: keywords
creator: Farisya Farhana Zulkiflee
creator: Siti Maryam Sharun
creator: Muhammad Firdaus Asyraf
creator: Saiful Bahri Mohamed
creator: Zuraidi Saad Saad
creator: Mat Sazilin Ayub
creator: Noor Hafizi Hanafi
creator: M. Sabri
creator: Rudi Kurniawan
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 91
ending page: 111
doi: 10.22153/kej.2026.12.016

Supply Chain Resilience in the Context of Global Production and Logistics
type: item
description: This paper presents an in-depth review of supply chain resilience in the context of global production and logistics, considering the immediate need for adaptive and robust systems in the growing global scenario of uncertainties. This study aims to investigate the needs and strategy of SCR for global production and logistics. First, 39 articles focusing on SCR in the context of global production and logistics are selected. The articles are then classified according to the resilience strategy proposed by the authors. From the selected works published, the study unravels the key themes, such as definitions of resilience, disruption drivers, integration of digital technologies (e.g. digital twins) and the importance of cybersecurity. The paper further discusses the drawbacks of lean manufacturing, resilience metrics and its industrial applications during momentous events such as COVID-19. The findings demonstrate that although applications of technology, such as AI and predictive analytics, have reinforced the resilience of production planning and control, many issues remain unaddressed in terms of the standardisation of metrics and coordinating sustainability with resilience. The paper presents recommendations endorsing hybrid resilience-efficiency models, cyber-resilience frameworks and multi-industry strategies to enable supply chains to face future disruptions.
subject: Articles
  topic: section
subject: supply chain resilience; production planning and control; hybrid resilience-efficiency models; cyber-resilience frameworks; multi-industry strategies
  topic: keywords
creator: Jaharah Ghani
creator: Mohd Fadzli Abdollah
creator: Muhammad Rizal
creator: Afifah Zakiyyah Juri
creator: Sulaiman Elias
creator: Aizat Abdul Rashid
creator: Qamarul Syahmi Kamaruzaman
creator: Nur Ain Najwa Zainudin
creator: Muhammad Nursyakirn Hairil Azmi
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-09-01
publication date: 2025-09-01
starting page: 97
ending page: 105
doi: 10.22153/kej.2025.08.002

Pulse Wave Speed Estimation: A Comparative Study of the PU-Loop Technique versus Tensile and Compliance Testing on Synthetic Elastic Tubes
type: item
description: Pulse wave speed (c) is a key variable in haemodynamics and cardiovascular system analysis. It is directly related to arterial elasticity and inversely to distensibility of the arterial lumen. The pressure–velocity loop (PU-loop), derived from simultaneous velocity (U) and pressure (P) measurements, is used to estimate c, leveraging the linear U–P relationship during early systole, when reflected waves are absent due to bifurcations and/or occlusion. The PU-loop method was validated using tensile and compliance testing. Tensile testing, using a load cell, estimated Young’s modulus (E) of synthetic elastic tubes, allowing c calculation using the Moens–Korteweg equation. Compliance testing under static pressure assessed the relation between P and volume (V). This relationship predicted the compression and contraction courses, validating the testing applied to the lumen of the particular flexible tube. Consequently—to calculate the distensibility of the flexible tube lumen—the change in internal lumen volume due to the change in P was used, hence c corresponding to the Bramwell–Hill equation. Despite limitations such as sample size of tested specimens, experimental conditions not fully physiological, sensitivity of testing due to measurement noise; neglecting anisotropicity and heterogeneity of vessels; not accounting for the full complexity of real arterial behaviour and the use of synthetic elastic tubes stiffer than in vivo lumens, the calculated c (ccalculated), using the PU-loop technique, was in agreement with values acquired from compliance- and tensile-testing-based calculations. According to compliance and tension testing, the velocity of c was 19.5±0.05 and 19.5±0.012.1 m/s, respectively. Using the PU-loop technique, it was 20±0.25 m/s. These results validate the PU-loop methodology in c assessment.
subject: Articles
  topic: section
subject: Bramwell–Hill equation; Compliance testing; Moens–Korteweg equation; PU-loop; Tensile testing.
  topic: keywords
creator: Wisam Hacham
creator: Falah Alzuwaid
creator: Wesam Al-Zubaedi
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-09-01
publication date: 2025-09-01
starting page: 106
ending page: 117
doi: 10.22153/kej.2025.08.003

Preparation, Characterization, and Evaluation of Electrospun Copolymer Nanofiber Membranes for Kerosene Removal from Aqueous Solution
type: item
description: The petroleum sector generates significant amounts of oily wastewater requiring proper treatment before being released into the environment. The high stability, hydrophilicity, and large surface areas of membranes provide great opportunities for treating oily wastewater. For this work, electrospun nanofiber (ESNF) membranes of polyacrylonitrile (PAN) and polysulfone (PSU) were manufactured and evaluated for their separation capabilities. Multiple concentrations of the PAN and PSU polymers were used to optimize the copolymer nanofiber (NF) membranes. The water flux, oil rejection, and porosity evaluation of the membranes were compared. In addition, the surface and profile of the ESNF membranes were evaluated in terms of morphology and roughness, wettability, and tensile strength, and the results of scanning electron microscopy, atomic force microscopy, contact angle, and tensile tests were incorporated. The ESNF membranes provided positive results in the separation of oily wastewater. In particular, the best results were observed for the membranes composed of 75% PAN and 25% PSU, which reached 99.7% oil rejection, a maximum water flux of 400 L/m2.h, and 96% porosity, demonstrating great potential for wastewater treatment on an industrial scale. However, this study focused primarily on short-term performance evaluation. The long-term mechanical and chemical stability of ESNF membranes under continuous operational conditions remains to be examined.
subject: Articles
  topic: section
subject: Polyacrylonitrile; Polysulfone; Copolymer; Electrospinning; Nanofiber membranes; Oily wastewater
  topic: keywords
creator: Wafaa Al-Musawy
creator: Basma Waisi
creator: Mustafa Al-Furaiji
creator: Ahmed Shehan
creator: Mehdi Jahangiri
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 1
ending page: 13
doi: 10.22153/kej.2026.12.010

Effect of Fibre Loadings on the Tensile Properties of PALF/PHBV Composite: A Two- Parameter Weibull Analysis
type: item
description: Interest in biodegradable and sustainable is steadily rising which has made the investigation into natural fibre reinforced biopolymers. Among these, poly(3-hyroxybutyrate-co-3-hydroxyvalerate) (PHBV) stands out for its biodegradability and biocompatibility, but its inherent brittle and has few or no properties conducive to application to wider engineering fields. Additionally. The natural variability of fibres can lead to inconsistent mechanical performance, yet only a few studies have examined this variability from a statistical perspective. In this study, the influence of adding pineapple leaf fibres (PALF) at different loading (10-40 wt.%) was investigated on the mechanical behaviour of PHBV. The tensile strength was analysed using a linearized two-parameter Weibull method to determine the scale (η) and shape (β) parameters, providing insight into both the typical strength and the variability of the composite’s performance. The results show that tensile strength increases with fibre loading, with highest of 46.54 MPa (±4.12) at 30 wt.% PALF, corresponding to a 73% rise in scale parameter compare to other loadings. A strong correlation was established between the experimental data and the Weibull model, with (R2 > 0.9) and the differences are below 1 % and 7 % for neat PHBV and PALF/PHBV respectively. Scanning electron microscopy (SEM) analysis revealed signs of fibre rupture and fibre extraction, indicating that the composite’s failure was a result of poor interfacial bonding between the matrix and fibre. The Weibull analysis able to provide more comprehensive evaluation of the composite mechanical reliability as it taken account the statistical distribution of all measured data rather than just the means value. The two-parameter Weibull model demonstrated that the composite containing 30 wt.% PALF attained the maximum tensile strength with satisfactory reliability. This finding signifies an ideal equilibrium between performance and variability, essential for th…
subject: Articles
  topic: section
subject: tensile strength; PALF; fibre loadings; Weibull distribution; PHBV polymer
  topic: keywords
creator: Zaleha Mustafa
creator: Ain Amirah
creator: Siti Hajar Sheikh Md Fadzullah
creator: Yan Farm
creator: Thanate Ratanawilai
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 136
ending page: 144
doi: 10.22153/kej.2025.11.006

Review of Current Trends, Materials and Challenges in 3D Printing for Biomedical Applications Using Polylactic Acid
type: item
description: Polylactic acid (PLA) is a widely used biomaterial in additive manufacturing (AM) because of its biodegradable and biocompatible nature and availability. Hence, this study focuses on the importance of PLA in biomedical applications such as personalised medicine, tissue engineering, drug delivery and customised implants and explores the possibility of PLA-based biomedical products. Different AM technologies, such as fused deposition modelling, stereolithography, and selective laser sintering, and their possible uses with PLA are presented. Apart from pure PLA, the use of composite materials based on PLA is important, as these materials can improve mechanical and biological properties, which are highly desirable for various clinical applications. Several critical issues, such as material properties, resolution, regulatory affairs, biodegradability and sustainability, are examined and discussed. In addition, the potential of PLA-based 3D printing technologies, including multimaterial 3D printing and bioprinting for biomedical applications, is explored
subject: Articles
  topic: section
subject: Polylactic acid; 3D printing; Biomedical applications; PLA-based composites; Tissue engineering; Fused deposition modelling; Bioprinting; Drug delivery; Personalised medicine
  topic: keywords
creator: Dwi Hadi Sulistyarini Suistyarini
creator: Effendi Mohamad1
creator: Mohd Rizal Alkhari
creator: Muhammad Ilman Hakimi Chua Abdullah
creator: Teruaki Ito
creator: Arfauz A Rahman
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 32
ending page: 53
doi: 10.22153/kej.2026.04.002

Sensitivity Analysis of the Integrated CRITIC-TOPSIS Method for Selecting Optimal Natural Fibre–Reinforced Composites for Toe Caps in Safety Footwear
type: item
description: The selection of natural fibres for toe caps in safety footwear requires careful evaluation of multiple performance criteria. This process makes the task a multicriteria decision-making problem, as numerous alternatives and requirements must be considered simultaneously. This study applied the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to compute objective criterion weights, which were then integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank and identify the optimum fibre amongst 12 alternatives. A sensitivity analysis was conducted to evaluate the impact of weight variations on the selection results. The combined CRITIC-TOPSIS method effectively determined the optimal natural fibre for safety toe cap applications. Sensitivity analysis confirmed the robustness of the results whilst highlighting the influence of criterion weight variations on final rankings. The proposed integration offers a systematic and reliable approach for material selection in safety footwear. The findings provide useful guidance for the development of natural fibre–reinforced polymer composites and support lightweight and sustainable alternatives for toe caps in safety footwear.
subject: Articles
  topic: section
subject: Natural fibre composite; Multi-criteria decision Making (MCDM); CRITIC; TOPSIS; Material selection
  topic: keywords
creator: Mohd Hidayat Ab Rahman
creator: Ridhwan Jumaidin
creator: Jamaluddin Mahmud
creator: Siti Mariam Abdul Rahman
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 152
ending page: 166
doi: 10.22153/kej.2026.01.001

Design of a Compact Broadband Metamaterial Absorber with Wide-Angle Reception and Polarization-Insensitive for Electromagnetic Energy Harvesting in Wireless Sensor Network Applications
type: item
description: This work introduces a novel design of a compact broadband metamaterial (MM) absorber with polarization-insensitive, wide-angle reception for ambient electromagnetic (EM) power harvesting applications. The proposed MM unit cell consists of two layers of low-cost FR-4 substrate. The top layer contains a resonator structure and four lumped resistors. A copper sheet, serving as a ground plane, is placed on the backside of the bottom layer to reduce transmission losses. The top and bottom layers are separated by a thin air layer to enhance absorption at operational frequencies. The overall size of the MM unit cell is 12×12×4.57 mm³, with measurements of 0.16λ₀×0.16λ₀×0.06λ₀ at the lowest frequency in the absorption spectrum. The unit cell’s input impedance is carefully engineered to align with that of free space, an approach facilitating efficient EM power absorption and suitable redirection to resistive loads. The absorber’s performance is assessed for different polarization and incident angles for transverse electric (TE) and transverse magnetic (TM) modes. The simulation results indicate that the introduced absorber attains broadband absorption, surpassing 90% in the frequency range of 4 GHz to 7 GHz. Moreover, the MM unit cell achieves harvesting efficiency above 80% under normal incidence and continues to exhibit outstanding performance over different incidence angles for the TE and TM modes. This study substantially improves MM-based energy harvesting through integrating wide-angle reception, high-absorption broadband, compact size, and polarization insensitivity, which make it an ideal option for supplying energy to wireless sensor networks.
subject: Articles
  topic: section
subject: Broadband; Metamaterial absorber; Polarization-insensitive; RF electromagnetic energy harvesting; Wide-angle reception; Wireless sensor network (WSN)
  topic: keywords
creator: Zainalabdin alchallaby
creator: Yahyaa alhusseini
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 55
ending page: 73
doi: 10.22153/kej.2026.12.014

Optimization of Phenol Adsorption Using Activated Carbon Derived from Spent Tea Leaves in a Packed-bed Column
type: item
description: This study investigates the potential of spent tea leaf-derived activated carbon (STLAC) as an adsorbent for phenol removal from simulated wastewater using a packed-bed adsorber. STLAC was prepared via chemical activation through impregnation with potassium hydroxide, followed by carbonization under controlled conditions. The physicochemical characterization of the prepared activated carbon has been confirmed. Response surface methodology (RSM) with central composite design (CCD) was utilized to optimize four operating parameters: flow rate, initial phenol concentration, bed height, and pH. A quadratic mathematical model was developed to describe the relationship between these parameters and removal efficiency. Results demonstrate the high phenol removal capacity of STLAC, achieving a maximum efficiency of 91.92% under optimal operating conditions: flow rate (11.46 mL/min), initial phenol concentration (19 mg/L), bed height (17.8 cm), and pH (6.8). The first breakthrough and saturation points were observed at 31 and 70 min, respectively. Regeneration studies have confirmed the activity of STLAC for reuse in the adsorption after five cycles, with an efficiency exceeding 75%. STLAC exhibited high efficiency in phenol removal, offering advantages in terms of sustainability and cost-effectiveness. To our knowledge, this study is the first to systematically optimize phenol removal using STLAC in a continuous packed-bed column using RSM-CCD methodology and utilize the optimized parameters for determining breakthrough characteristics. This study underscores the potential of converting agricultural waste into value-added products for environmental remediation. These results demonstrate that STLAC is a promising, low-cost, and sustainable adsorbent for removing phenol from industrial wastewater.
subject: Articles
  topic: section
subject: Breakthrough curve; Packed-bed adsorber; Response surface methodology; Regeneration studies; Spend tea leaves; Wastewater
  topic: keywords
creator: Ihsan Habib Dakhil
creator: Ghazi Naser
creator: Ahmed A. Hadi Hadi
creator: Ahmed Hassan Ali
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 14
ending page: 29
doi: 10.22153/kej.2026.12.011

Analytical and Mechanical Study of Transtibial Osseo-Integrated Prostheses Implant
type: item
description: In amputation surgery, osseointegration is the placement of a metal implant in the residual bone and is conducted with an external prosthesis. In this study, the prospective application of Ti–13Nb–13Zr alloy in a transtibial osseointegrated prosthesis was carefully examined. The alloy demonstrated favourable mechanical properties, such as strong mechanical strength with an average yield strength of 482 MPa, ultimate tensile strength of 551.843 MPa and modulus of elasticity of 74 GPa. In compression testing, the material showed its resilience to compressive loads, exhibiting 700 MPa yield strength and 1010 MPa compressive strength. The elastic modulus of Ti–13Nb–13Zr alloy is approximately 55–65 GPa, which is much closer to that of human bone (10–30 GPa) compared with that of Ti–6Al–4V alloy (110 GPa). This proximity reduces stress shielding, a common issue in implants that a mismatch in stiffness between the implant and bone leads to bone resorption. Analyses using the finite element method demonstrated uniform stress distribution, safety factors and minimal deformation for a range of implant sizes, guaranteeing structural integrity and functionality. The maximal von Mises stress on the tibia bone and implant did not surpass the yield tensile stress of the titanium alloy and bone, which is 470 and 175 MPa, respectively; the maximum safety factor at L=120 and D=3 and the stress levels are not expected to induce material failure. These findings bring up new possibilities for enhanced prosthetic design and use in the field of biomedical engineering by demonstrating the alloy’s potential suitability for osseointegrated prosthetic applications.
subject: Articles
  topic: section
subject: Keywords: Implants; osseointegration; prosthetics; bone attachment; Ti–13Nb–13Zr alloy.
  topic: keywords
creator: Saif Abbas
creator: Jumaa Chiad
creator: Ayad Takhakh
creator: Kadhim Resan
creator: Abdullah Fatlawi
creator: Alaq Saad
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 1
ending page: 12
doi: 10.22153/kej.2025.09.005

Thermal Comfort and Internal Conditions Testing of Standard Relief Tents in cold Climates
type: item
description: To assist decision-makers in selecting the best solutions and arriving at better solutions for temporary shelter and to identify key issues related to temporary shelter methods and provide some principles and recommendations, this study presented a theoretical investigation into the measurement of thermal comfort in standard relief tents in cold climates. The results were analysed and compared using the CBE MRT and CBE Thermal Comfort tools based on field-measured values inside a tent from December 2024 to February 2025. The results on 11, 13, 14 and 18 December 2024, were selected to avoid repetition. Thermal comfort is a key factor in the design and use of tents in cold regions because of its direct impact on the safety and well-being of occupants. Factors affecting the thermal comfort of tents in cold weather include insulation, ventilation, clothing and heating. This study was applied in nature and was conducted using a descriptive analytical approach. Data were collected using library and documentation methods. This study provides important indicators in the field of thermal comfort, particularly in the design of heating, ventilation and air conditioning systems. These environmental and individual factors influence the relationship between the predicted population mean vote and the predicted dissatisfaction rate. On the basis of these factors, the study helps measure human indoor thermal comfort and determine the influencing factors in each case to reach the desired conclusions while emphasising that the tents did not provide suitable thermal conditions for their inhabitants until they were treated using an experimental model.
subject: Articles
  topic: section
subject: temporary shelter; thermal comfort; CBE MRT; CBE Thermal Comfort tools; PMV; PPD
  topic: keywords
creator: Rawan A.Fayyad
creator: Issam M. Ali Aljubury
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 125
ending page: 135
doi: 10.22153/kej.2025.08.004

Real-Time Adaptive Traffic Signal Control with YOLOv10 and Image Processing
type: item
description: Traffic lights operating on a fixed schedule are mostly time-consuming; for example, running green signals in the absence of vehicles, leading to a buildup of long queues at red lights. This inefficiency results in congestion in cities, contributes to delays and economic losses and intensifies pollution levels. In this study, a deep learning-based adaptive image processing traffic light control system for real-time dynamic regulation of signals was proposed. Different from typical sensor-based solutions, the proposed method uses established surveillance cameras, enabling cost-efficient deployment and easy installation. A YOLOv10-based detection model identifies and classifies vehicles by type, applying weight factors to effectively estimate traffic demand. A dynamic timing algorithm enables continuous redistribution of green-light durations due to existing unbalances in the flow for any or all intersection phases. A practical microcontroller-based system might be integrated directly into the existing infrastructure. For assessment, the model used data from 12,500 images labelled accordingly and divided into the following: 70% for training, 15% for validation and 15% for testing. The model was assessed in a SUMO-based simulation of a very busy four-way intersection and actual deployment in Baghdad, Iraq. Compared with fixed time control, this adaptive system reduced vehicle wait time by up to 91.7%. Furthermore, results indicate reduced fuel consumption and CO2 emissions, thereby leading to considerable economic and environmental benefits. Overall, the proposed framework represents a practical and scalable implementation for modern traffic management, overlooking possible implementations of enhancements such as prioritisation of emergency vehicles and multi-intersection coordination.
subject: Articles
  topic: section
subject: Intelligent traffic lights control; Deep learning; Image processing; Traffic flow optimisation; Smart cities.
  topic: keywords
creator: Ammar Majeed
creator: Suhad Naeem
creator: Elaf Saeed
creator: Abdulrahman Al-Shanoon
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 65
ending page: 81
doi: 10.22153/kej.2025.09.006

Levofloxacin Adsorption and Release on γ-Mesoporous Alumina Nanoparticles Using Linear Kinetics Equations
type: item
description: Mesoporous alumina is a promising drug carrier because of its extensive surface area, pore structure, biocompatibility and non-toxicity, facilitating high drug loading and release capacity. This study aimed to investigate the adsorption and release kinetics of levofloxacin (LF) on prepared γ-mesoporous alumina nanoparticles (γ-MNPs). Adsorption kinetic tests were carried out by a batch method, drug loading was investigated using an impregnation method, and the release kinetics were determined using a dialysis bag maintained at 310 K. Adsorption kinetics were analysed using three kinetics models: the pseudo-first-order, pseudo-second-order and intraparticle diffusion. Results showed that the adsorption of LF followed a pseudo-second-order reaction, with the adsorption rate and mechanisms regulated by two consecutive steps, suggesting that intraparticle diffusion was involved, but it was not the only rate-controlling step. Drug loading results showed that the amount of LF loaded into γ-MNP was 2.17 mg drug/g γ-MNP. The drug release behaviour was investigated at pH 7.4 and pH 5.4. The drug release profile for the LF/γ-MNP system showed that the drug release percentages were 18.00% at pH 5.4 after 48 h and 90.41% at pH 7.4 after 24 h. Drug release was characterised by the kinetic models, including zero-order and first-order kinetics. The data from the pH 5.4 system was best fitted by zero- and first-order kinetics. The drug release mechanisms from a matrix were interpreted using Korsmeyer–Peppas, Kopcha and Higuchi models. The results demonstrated that drug release at pH 7.4 exhibited non-Fickian diffusion, whereas that at pH 5.4 was Fickian diffusion. The Hugichi constants (kH), which were correlated with the release rate, were low in the two systems, confirming the results of zero- and first-order equations. Therefore, γ-MNP is a promising and effective delivery medium for LF treatment.
subject: Articles
  topic: section
subject: Mesoporous alumina; Levofloxacin adsorption; Korsmeyer–peppas model; Levofloxacin loading; Drug release
  topic: keywords
creator: Maryam A. Azeez
creator: Sameer H. Kareem
rights: Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 141
ending page: 152
doi: 10.22153/kej.2026.10.002

Synthesis and Characterization of Flash Graphene for Use in Remediation of Contaminants in Iraqi Groundwater Using Batch Adsorption Techniques
type: item
description: This research aims to assess the behavior and effectiveness of flash graphene (FG) in the remediation of pH, EC, total dissolved solids (TDS), cations, and Fe+3 from real groundwater (GW) of the wells of the Al-Raeed Research Station, which is used in reclamation and irrigation. enclose that contaminant levels in remediated real GW abide criterion of Iraqis and Food and Agriculture (FAO) standards. Adsorption technology in the batch adsorption mode experiment was achieved by FG, a carbon-based, highly porous structure that has strong adsorption capacity due to its beneficial surface area (71.7 m2/gm). A locally designed and manufactured electroflash reactor was used to synthesize FG by converting activated carbon derived from banana peels. In this process, banana-peel activated carbon (BPAc) was exposed to a mild spark and 8–10 circuit break shocks per reactor run. The FG morphology and functional adsorption groups were characterized via scanning electron microscopy, Fourier transform infrared, and X-ray diffraction tests. The remediation efficiencies in the batch experiments were 69%, 69%, 61%, and 100% for Ca, Mg, TDS, and Fe, respectively, at an optimum FG dosage of 1.5 g, contact time of 4 hours, 150 rpm, and pH of 7. The adsorption capacity, which followed the Freundlich isotherm model, was Ca+2 = 14.6 mg/g, Mg+2 = 16.4 mg/g, Fe+6 = 0.074 mg/g, TDS = 7.3 mg/g. Finally, with the synthesized FG used and the parameters (dose, agitation speed, pH, and contact time) adjusted, the batch mode experiments on real GW samples yielded pH, EC, T.D.S., Mg+2, Ca+2, and Fe+3 values, which satisfied FAO restrictions and Iraqi standards.
subject: Articles
  topic: section
subject: adsorption; cation; flash graphene; BPAc; groundwater; contaminant; synthesize.
  topic: keywords
creator: Noora Subhi
creator: Hayder Abdul-Hameed
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 13
ending page: 31
doi: 10.22153/kej.2025.07.001

Impact of the Number of Reflecting Elements and Positioning of the Reconfigurable STAR-Passive-ORIS in MIMO FSO Systems
type: item
description: This study aims to improve the performance of reconfigurable STAR-passive-OIRS in multiple input multiple output-based free-space optical (FSO) communication systems. The study focuses on analysing the influence of two main factors: the number of reflective elements and the location of the smart surface. STAR-RIS technology has shown great potential in improving spectrum efficiency and signal-to-noise and interference ratio (SINR) and in increasing channel gain by providing 360° coverage and reducing interference in environments with line-of-sight constraints. Results reveal that increasing the number of reflective elements enhances signal strength and improves SINR, whilst placing the smart surface closer to the source or receiver achieves optimal performance. The study highlights STAR-RIS technology’s ability to overcome traditional FSO systems’ limitations and improve network performance in modern wireless communication environments.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Free space optics (FSO); Intelligent reflecting surface (IRS); Nonorthogonal multiple access (NOMA); Radio frequency (RF); and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS).
creator: Batool Natiq
creator: Lwaa Abdulameer
creator: Mohammad Dowlatshahi
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-06-01
publication date: 2025-06-01
starting page: 106
ending page: 116
doi: 10.22153/kej.2025.02.004

Autonomous Vehicle Steering-based Feedback Linearization and Sliding Mode Control
type: item
description: This study proposes a robust control approach for vehicular dynamic system speed control. The proposed method combines a sliding mode controller with a feedback linearization technique. Considering the high nonlinearity of the vehicle dynamic system model, feedback linearization is used to transform the vehicle dynamic system into a linear system. A Lyapunov theorem is used to approve the stability of the proposed controller. Moreover, a proportional integral derivative (PID) controller with genetic algorithms is used for comparison. The integral absolute error (IAE) is used as the performance comparison index between controllers. Simulation results show that the proposed method can achieve excellent performance with high robustness against external disturbance and system uncertainty. In the tracking case, the IAE value of the proposed controller is 2.3, whilst that of the PID is 15.2. Under external disturbance, the IAE values are 3.1 and 19.1 for the proposed controller and PID, respectively.
subject: Articles
  topic: section
subject: Feedback Linearization, Robust control , SMC.
  topic: keywords
creator: Anssam Kames
creator: Ali Mary
creator: Laith Mayyahi
creator: Oday Al-Jerew
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-09-01
publication date: 2025-09-01
starting page: 48
ending page: 54
doi: 10.22153/kej.2025.05.002

Biodiesel Production from Castor Oil
type: item
description: Biodiesel is becoming one of the most attractive alternative biofuels for petroleum-based diesel fuels. The castor plant is one of the abundant non-edible oils found in many countries. This paper aims to study Libyan castor oil and its potential for diesel conversion. Experiments were carried out in the laboratories of the Specific Center for Training in the Oil Industries in Al-Zawiya. The oil was extracted using a Soxhlet extractor and n-hexane solvent at 60 °C. Transesterification reactions were conducted in a batch reactor (a three-neck flask was used, where the middle opening carries a reflux condensation unit) at 65 °C. The methanol-to-castor oil molar ratio was 6:1, with a catalyst concentration of 1 wt.% relative to the castor oil, and the reaction time was 30 min. Castor oil was analysed and found to have a fatty acid content of 0.7%. The productivity of biodiesel exhibited 80% yield. Standard test methods of analysis were conducted to determine the biodiesel’s properties. Results indicated that the kinematic viscosity at 40 °C was 14.24 cSt, the density was 0.924 g/cm3, the cetane number (CN) was 54 and the pour point was −15 °C, indicating that castor oil biodiesel is of high quality, with a high CN and a low pour point.
subject: Articles
  topic: section
subject: Biodiesel; Castor oil; Alkaline transesterification; Renewable energy; and non-edible oil.
  topic: keywords
creator: Nadya Hussin AL Sbani
creator: Awatif Meelad
creator: Omar sultan
creator: Alaa Mohammed
creator: Amani Kwadikha
creator: Saleh Tweib
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-06-01
publication date: 2025-06-01
starting page: 1
ending page: 11
doi: 10.22153/kej.2025.03.001

Effect of Heat Treatment on the Microstructure, Impact Toughness and Hardness of High-Chromium White Cast Iron with Added Niobium and Manganese Minerals
type: item
description: In this work, manganese and niobium were added to high-chromium white cast iron, from which drilling and grinding tools were made. Two melts with different proportions of manganese and niobium (manganese 2.75% and niobium 0.49%, manganese 3.5% and niobium 1.1%) were used. High-chromium white cast iron is considered one of the most important alloys in heavy-duty applications, and its properties are affected by the added alloying elements and heat treatment. Niobium can increase corrosion resistance and hardness, form carbides, enhance thermal conductivity and improve response to heat treatment. As for manganese, it can increase impact toughness, remove oxygen from iron oxide and reduce flexibility. The heat treatment was carried out in accordance with two different programmes: 1- heating to 950 °C with a holding period of 2 h and cooling in atmospheric air; 2- heating to 960 °C with a holding period of 20 min, then cooling in the oven and returning to 425 °C for 3 h. The mechanical properties, such as wear resistance, impact durability and hardness, before and after heat treatments 1 and 2 were compared.
subject: Articles
  topic: section
subject: Wear; impact toughness; hardness; grinding; high-chromium white cast iron; cracking
  topic: keywords
creator: Ali Hatra
creator: Ahmad Salamah
creator: Nassim Aldaher
rights: Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2025-03-01
publication date: 2025-03-01
starting page: 73
ending page: 88
doi: 10.22153/kej.2025.02.001