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: Performance Evaluation and Optimisation of Spectrum Management in Communication Systems by GA
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: 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: Effect of Fibre Loadings on the Tensile Properties of PALF/PHBV Composite: A Two- Parameter Weibull Analysis
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: 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
items: K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm
items: Analyzing Geothermal Heat Exchangers Using Comsol Non-Isothermal Pipe Flow Model
items: Removal of Methylene Blue from Wastewater Using AL Haji Plant as a Low-Cost, Eco-Friendly Adsorbent
items: Preparation and Characterisation of Activated Carbon from Pomegranate Waste Using Chemical with Microwave Activation
items: Application of Aqueous Two-Phase System in the Extraction of Invertase Enzyme from Potato Tubers Using PEG8000/Potassium Phosphate
items: Development and Implementation of an Automated Production Line Training System
items: Improving the Performance of Steering by Wire Using a Model Predictive Controller Enhanced with Particle Swarm Optimisation
items: Object Dimension and Location Determination in Gantry Robots and Conveyor Systems: A Review
items: Secure and Lightweight Cipher for Resource-constrained IoT Healthcare Applications using Snake Key Generation
items: Examination the Corrosion Inhibition of Synthesized Azo-Schiff Bases Compounds from (P-Fluorophenyl)-1,3,4-Thiadiazoles for Carbon Steel Alloy in Acidic Medium
items: Path Loss Models for 5G Wireless Communication System Using mmWave Communication Channel
items: Impact of Design Parameters in Thermal Solar Water Storage Tank Systems: (A Review)
items: Enhancing the Surface Mechanical Properties of AL2618 Alloy Turbochargers Using a PLD Device with AI2O3
items: Deep Learning Model for Prediction of Dementia Severity based on EEG Signals
items: Deep-Learning-Based Mobile Application for Detecting COVID-19
items: Violence Prediction Estimation in Surveillance Cameras Using CNN with GAMMA Correction

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

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

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

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

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

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

K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm
type: item
description: Phishing detection is considered a critical problem in cybersecurity, and utilising machine learning with an efficient feature selection method for precisely identifying malicious websites is deemed the most critical challenge. This research presents a two-phase phishing detection system by employing unsupervised feature selection and supervised classification. In the first phase, the best set of features is identified by the Genetic algorithm and is utilised by the K-means clustering algorithm to divide the dataset into groups with similar traits. In the second phase, the best set of features in each group is identified through the Genetic algorithm to enhance the classification process. Finally, a voting ensemble technique is applied, in which the Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Adaptive boosting (AdaBoost) models are combined. Predictions are aggregated using a soft voting mechanism. This research utilises the web page phishing detection dataset, which consists of 11,430 URLs with 87 features. From the results, an accuracy of 99% is achieved using the voting ensemble technique with feature selection compared with 77.3% without feature selection. The model performance experiences a significant boost through the GA-optimised feature selection by reducing computational complexity and improving key metrics such as accuracy, precision and F1-score. Additionally, the performance across four clusters demonstrates the positive impact of K-Means clustering in improving classification accuracy for specific data groups. As proven by the obtained results, integrating feature selection with ensemble learning is effective for phishing detection; moreover, the scalability and efficiency of such a solution in real-world applications are demonstrated.
subject: Articles
  topic: section
subject: AdaBoost; ensemble learning; feature selection; genetic algorithm; K-means clustering; machine learning; phishing detection
  topic: keywords
creator: Ali Al-Hafiz
creator: Adnan Jabir
creator: Shamala Subramaniam
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: 117
ending page: 135
doi: 10.22153/kej.2025.04.011

Analyzing Geothermal Heat Exchangers Using Comsol Non-Isothermal Pipe Flow Model
type: item
description: Earth–air heat exchangers (EAHE) hold great promise for reducing typical air conditioning systems’ energy consumption whilst preserving high indoor comfort. The present analyses a 3D model using COMSOL Multiphysics software for a geothermal heat exchanger to examine the thermal behavior along the piping system. The experimental findings of the real EAHE in Baghdad City during January and June are transferred into the nonisothermal pipe flow interface for modeling temperature, velocity and pressure distributions along the piping system. The temperature variation of subsurface soil and the radial temperature distribution around the pipe are modeled into the heat transfer interface. The magnitude of heat flux is also computed in different times along the pipe. The effects of the continuous operation of EAHE on the output temperature and on the soil temperature around the pipe are also considered. The model’s output demonstrates that the air temperature rise in January is 10 whereas the air temperature drop is 14 in June. The effect of extracted/absorbed heat transfer from/to air in pipes is extended up to 0.7 m in the radial direction of the soil surrounding pipes due to continuous airflow in EAHE.
subject: Articles
  topic: section
subject: geothermal energy, sandy soil, earth heat exchanger, COMSOL, thermal analysis
  topic: keywords
creator: Mohammed A. Atiya
creator: Ali Naser Hussien
creator: Azher M. Abed
creator: Philippe Mandin
creator: Farkad Ali Lattieff
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: 1
ending page: 12
doi: 10.22153/kej.2025.01.001

Removal of Methylene Blue from Wastewater Using AL Haji Plant as a Low-Cost, Eco-Friendly Adsorbent
type: item
description: This study investigates the use of agricultural waste, specifically the Al Haji plant, for wastewater treatment. The term 'Al Haji plant' refers to species from the Al Haji genus, particularly "Al Haji maurorum " and "Al Haji canescens ", which are drought-resistant plants of the Fabaceae family. Traditionally, these plants are known for their medicinal and antimicrobial properties. In this research, waste from the Al Haji plant was converted into activated carbon (AC) as a low-cost and techno-economically viable biosorbent for the removal of methylene blue (MB) dye from wastewater. The production of AC involved two key steps: chemical activation using sodium hydroxide (NaOH) and carbonisation at 300 °C. The properties and adsorption potential of the resulting AC were investigated. Various parameters were studied, including pH, contact time, stirring rate, initial dye concentration, adsorbent dosage, and temperature. Characterisation of the biosorbents was performed using Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) surface area analysis. The SEM investigation showed notable pores in the Al Haji plant sample that could enhance the absorption of the MB dye. Results indicated that the BET surface area for physical AC is 25.5796 m²/g. The surface area of chemical AC was reported as 16.2767 m²/g. The dye exhibited pseudofirst-order adsorption kinetics, with average rate constants of 0.020082 and 0.001589 for physical and chemical activation approaches, respectively. An adsorption isotherm study was performed using the Langmuir model, which showed an R² value of 0.827 and a maximum adsorption capacity of 5.24 mg/g." The study of adsorption thermodynamics revealed that MB adsorption on Al Haji activated carbon is spontaneous and endothermic.
subject: Articles
  topic: section
subject: Al Haji plant; Batch sorption; Kinetic models; Methylene blue; Thermodynamic Parameter.
  topic: keywords
creator: Ruqaya Adday
creator: Hussein Flayeh
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: 23
ending page: 41
doi: 10.22153/kej.2025.02.002

Preparation and Characterisation of Activated Carbon from Pomegranate Waste Using Chemical with Microwave Activation
type: item
description: This study presents the preparation method and characterisation of activated carbon (AC) from pomegranate waste by using chemical with microwave activation. Pomegranate peels in powder form were impregnated in 40–60 %wt sulphuric acid with different ratios (1:3 of pomegranate to sulphuric acid) and times (2–6 h) for chemical activation. The impregnated samples were activated in microwave under inert atmosphere at different powers (450–900 W) and activation times (7–21 min) to produce AC. Design-Expert software (version 13) was selected with the I-optimal method to analyse the effects of AC preparation variables. The preparation variables, including acid concentration, impregnation time, impregnation ratio, microwave power and activation time, with their effects on the adsorption capacity of methylene blue dye, were investigated. On the basis of the obtained maximum methylene blue number, the AC and raw material were characterised via Brunauer–Emmett–Teller test, field emission scanning electron microscopy with energy-dispersive X-ray analysis, Fourier transform infrared spectroscopy and X-ray diffraction analysis. Results indicate that the AC produced from pomegranate peels achieves a sufficient surface area of 1431.8 m2/g and a total pore volume of 1.6236 cm3/g with a spongy and amorphous nature with minor crystal characteristics. It can be used as a low-cost product with desirable surface characteristics. These results promote the use of AC prepared from pomegranate waste as an obtainable and low-cost adsorbent.
subject: Articles
  topic: section
subject: adsorption; cation; flash graphene; BPAc; groundwater; contaminant; synthesize.
  topic: keywords
creator: Ali Hanoon
creator: Sami Salman
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: 32
ending page: 44
doi: 10.22153/kej.2025.11.005

Application of Aqueous Two-Phase System in the Extraction of Invertase Enzyme from Potato Tubers Using PEG8000/Potassium Phosphate
type: item
description: An aqueous two-phase system (ATPS) is a clean alternative for traditional aqueous-organic solvent extraction systems. This technique has proven to be highly effective for extracting and separating mixtures of biomolecules. The present investigation involved the extraction of the invertase enzyme from potato tubers utilizing ATPS. The system applied was: Polyethylene glycol 8000 combined with dipotassium diphosphate (PEG8000/PPH). The study investigated the impact of five factors, namely temperature, PEG8000 concentration, dipotassium phosphate concentration, pH, and the addition of sodium chloride (NaCl) or magnesium sulphate (MgSO4) as a catalyst, on the recovery percentage (%Rec) and partition coefficient (KE) of the invertase of the invertase enzyme in ATPS over the study period. The system achieved a maximum recovery (%Rec) of 87.52% and a maximum partition coefficient (KE) of 7.01 at a temperature of 10°C, with a PEG8000 concentration of 1.5 g/10 ml, a dipotassium phosphate concentration of 2.4 g/10 ml, and a pH of 10. After the addition of neutral salts, the system achieved a maximum %Rec of 91.9% and a maximum KE of 11.34 under the same optimal conditions, and a concentration of MgSO4 of 1 g/10 ml.
subject: Articles
  topic: section
subject: Aqueous two-phase system, Enzymes, plant source, Potato tuber.
  topic: keywords
creator: Meaad Kadhim
creator: Khalid Hameed
creator: Hameed Mahood
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: 1
ending page: 11
doi: 10.22153/kej.2025.11.003

Development and Implementation of an Automated Production Line Training System
type: item
description: Industrial automation has significantly enhanced productivity, efficiency, and product quality by integrating advanced technologies such as robotics, artificial intelligence, and IoT. This study focuses on rehabilitating and developing the DL-MS6000 automatic industrial production line training system at the Ministry of Higher Education and Scientific Research in Baghdad, Iraq. The system, comprising six stations controlled by six PLCs, had issues, including hardware failures, the need for periodic updates, and a lack of SCADA control. The development process replaced the six PLCs with a single CYMON PLC and eliminated the need for CC-Link network devices, reducing system complexity and cost. The system was optimized using a new control algorithm. The system achieved high precision, zero steady-state error, and a settling time of 40 ms by using the Root Locus method in the MATLAB program. Additionally, a SCADA/HMI system was implemented to enhance control and monitoring. The newly developed system offers increased reliability, cost-efficiency, and ease of use, making it a suitable tool for educational purposes in university training rooms.
subject: Articles
  topic: section
subject: Automated production line; Programmable logic controller; Training system; SCADA .
  topic: keywords
creator: Ali Noori
creator: Faiz Fawzi
creator: Hussain Tabina
creator: Egorov Nikolaevich
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: 53
ending page: 71
doi: 10.22153/kej.2025.02.003

Improving the Performance of Steering by Wire Using a Model Predictive Controller Enhanced with Particle Swarm Optimisation
type: item
description: The challenges of steering-by-wire (SBW) systems in vehicles are due to the absence of a direct mechanical link between the steering wheel and the wheels on the road. This limitation imposes the necessity of employing sophisticated control systems to attain the highest accuracy and stability during operation. In such systems, the responsibility rests completely on the utilised controller to change the wheel’s angle on the road swiftly and accurately in response to the steering wheel changes by the driver. However, conventional control systems suffer slowly in responding to instructions and some fixed errors in their steady-state phase. The current study introduces an innovation of a model that integrates model predictive control (MPC) with particle swarm optimisation (PSO) to improve the performance of SBW systems. The MPC procedure is typically employed to control system responses over a timeframe and eliminate unnecessary and ineffective actions according to the specified objectives. The PSO algorithm is used to manage the ineffective parameters within the MPC. Results revealed that the proposed approach remarkably and effectively shortens response time, enhances wagon stability and reduces the settling error to nearly null. In addition, the integration of PSO with the overall system performance enhances the tuning of the response time, hence augmenting the system efficiency and responsiveness. The study outcomes support the proposal that the control strategy can improve the efficiency of SBW systems with high operational goals.
subject: Articles
  topic: section
subject: Steering by wire, Vehicle, Model predictive controller, Particle swarm optimisation, Controller.
  topic: keywords
creator: Taher Alhassany
creator: Ali Hussien Mary
creator: Furat Ibrahim Hussein
creator: Mohammed Ghufran Khidhir Abboosh
creator: Muhammad Umar Fareed
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: 61
ending page: 72
doi: 10.22153/kej.2025.01.002

Object Dimension and Location Determination in Gantry Robots and Conveyor Systems: A Review
type: item
description: A​‍​‌‍​‍‌ gantry robot is one of the most common types of industrial robots with linear movement. This type of robot is also known as a Cartesian or linear robot. It is an automated industrial system that moves along linear paths, enabling it to create a 3D envelope of the space in which it operates. A robot of this type has a standardised configuration process because it can have several sets of axes, such as X, Y and Z. The gantry robot picks up products from several places, so it can search through various locations. Afterwards, it carefully deposits the products on a conveyor belt for the next stage of the procedure or final ​‍​‌‍​‍‌shipment. This integration enables continuous and automated material flow, increasing overall productivity and efficiency in manufacturing operations. Dimension measuring and object placement are critical tasks in robot and conveyor systems that depend substantially on the types of sensors. Progress in sensors and camera technologies has markedly enhanced precision, efficacy, productivity and adaptability. Sensors are commonly classified as range and vision sensors on the basis of their mode of measurement. This review article offers useful knowledge on gantry robots and conveyor systems and their historical background. It examines recent research that has reported on the advantages and disadvantages of gantry robots. Moreover, literature on object dimensioning and positioning via sensors and cameras in gantry robots is presented. This review elucidates the advantages and disadvantages of several sensing types, including cameras and conventional sensors. Comparisons of robotic systems in terms of accuracy, speed, cost, energy efficiency and other aspects are also performed. Many studies have been conducted on body detection via vision technology and sensors, but the use of laser sensors has received minimal attention and needs further focus in the future.
subject: Articles
  topic: section
subject: Gantry robot; Dimensions; Location; Sensors; Vision; Conveyor system
  topic: keywords
creator: Hayder Saadi
creator: Nazar kais ALKARKHI
creator: Wisam Abbood
creator: Somer Nacy
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: 106
ending page: 124
doi: 10.22153/kej.2025.04.012

Secure and Lightweight Cipher for Resource-constrained IoT Healthcare Applications using Snake Key Generation
type: item
description: The integration of the Internet of Things (IoT) in healthcare has transformed the monitoring of physiological parameters, significantly enhancing the capabilities of medical diagnostics and patient care. This study explores the use of advanced cryptographic techniques to secure IoT healthcare systems, particularly through block chain (BC) technology. Moreover, this study evaluates the effectiveness of two cryptographic key generators—bee swarm key generator and snake key generator—in enhancing data security within a BC-based framework for remote patient monitoring. Our study conducts a series of NIST randomness tests to compare the performance of these key generators in terms of randomness, resilience to cryptographic attacks and computational efficiency. Results show that the snake key generator outperforms the bee swarm key generator, demonstrating higher randomness in key generation and better secure coverage of the orientation space. This finding is crucial for securing important healthcare data, especially given the complexity of IoT devices, which transmit vital patient health information. Furthermore, this study examines the double-layer encryption architecture used to secure data in at rest and during transmission from a sensor’s gathering location to long-term storage. This approach is essential to preserve the privacy and integrity of patient data, which is critical for maintaining trust in IoT healthcare systems. This study aims to provide a comprehensive and comparative analysis to support the continued evolution of secure, efficient and scalable cryptographic solutions within IoT healthcare, demonstrating the importance of selecting the most effective key generation technique to address the unique security challenges of this new field.
subject: Articles
  topic: section
subject: IoT healthcare; blockchain technology; cryptographic key generation; data security; NIST randomness
  topic: keywords
creator: Saja Kareem Ismael
creator: Mohamed Ibrahim Shujaa
creator: Ahmed Bahaaulddin A. Alwahhab
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: 35
ending page: 48
doi: 10.22153/kej.2025.09.002

Examination the Corrosion Inhibition of Synthesized Azo-Schiff Bases Compounds from (P-Fluorophenyl)-1,3,4-Thiadiazoles for Carbon Steel Alloy in Acidic Medium
type: item
description: New azo-Schiff base compounds were prepared by different processes using (p-fluorophenyl)-1,3,4-thiadiazoles, and their role as corrosion inhibitors for carbon steel alloy in acidic media such as 0.1 M HCl at 298 K was investigated. Spectroscopic methods such as FT-IR and NMR were used to characterise the compounds, and potentiodynamic polarization curves were employed to evaluate their corrosion inhibition. p-Aminoacetophenone was react withed 2-(5-(4-fluorophenyl)-1,3,4-thiadiazol-2-yl) acetohydrazide (A1) to prepare N ′-(1-(4-aminophenyl) ethylidene)-2-(5-(4-fluorophenyl)-1,3,4-thiadiazol-2-yl) acetohydrazide (A2). Azo-Schiff bases (A3-A6) were synthesised by combining the prepared diazonium salts with different phenols. These compounds’ structures were changed to improve their ability to prevent corrosion in a range of industrial settings, especially for metals exposed to acidic environments. The inhibitory efficiency percentage was between 76% and 90%. At 90%, the synthetic medication (A6) obtained the highest value. The formed protective adsorption coating on the steel surface was the reason for the improved inhibitor activity. These findings imply that the artificial Azo-Schiff bases show promising application as corrosion inhibitors for carbon steel in acidic settings. Applications in sectors where corrosion resistance is crucial may benefit from this knowledge.
subject: Articles
  topic: section
subject: Potentiodynamic, Polarisation, Thiadiazol, Aminoacetophenone, Hydroxy naphthalene, Carbon steel, Fourier Transform Infrared Spectroscopy, Dimethyl sulfoxide.
  topic: keywords
creator: Khadija Zaidane
creator: Ahmed Naser
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: 12
ending page: 24
doi: 10.22153/kej.2025.09.004

Path Loss Models for 5G Wireless Communication System Using mmWave Communication Channel
type: item
description: Multiple-input multiple-output (MIMO) antenna topologies in wireless communication systems can increase channel capacity and dependability. The use of millimeter (mm)-wave frequency bands in conjunction with other technologies is a substantial step in achieving high data rates for sophisticated AI and multimedia devices. However, transmitting mm wave frequency signals presents challenges such as fading, time delay, propagation and scattering loss. Therefore, optimising propagation parameters is critical to improving performance. This paper examines the influence models for path loss (PL): ABG and CI on the design and analysis of a 4×4 MIMO W-OFDM system employing 16-QAM modulation technique for 5G wireless communication. This paper examines the system using the CI and ABG PL models to assess PL in dB, normal distribution and standard deviation. This paper uses 16-QAM digital modulation, and the simulation results show that the CI PL model-based MIMO W-OFDM system is very robust and successful in signal recovery.
subject: Articles
  topic: section
subject: MIMO, millimeter wave, path loss, ABG model, two-parameter CI model
  topic: keywords
creator: Zinah Dawood
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: 89
ending page: 99
doi: 10.22153/kej.2025.06.001

Impact of Design Parameters in Thermal Solar Water Storage Tank Systems: (A Review)
type: item
description: This work provides a historical overview of water storage tanks used for solar energy collection, with a focus on the impact of different geometric configurations on thermal performance and energy storage efficiency. The designs of cylindrical, spherical, rectangular and triangular tanks were reviewed to analyse their effects on thermal gradients, heat retention and energy storage capacity. Within the scope of nominal processes, the study primarily aimed to identify the factors influencing the choice of tank shape in terms of size, capacity and aspect ratio. Another key objective was to assess what enhances heat storage in the tank to improve thermal efficiency. Comparing cylindrical shapes to other shapes, such as spherical, rectangular, and triangular , it offered the optimal performance with the needed requirements for the objects that have a smaller surface area-to-capacity ratio in addition to the lower heat loss. Furthermore,when the tilt angle with horizontal axes of the storage tank was at 0°, the tanks’ ratio reduction had remarkable impact on thermal stratification . The results might provide valuable information for the thermal storage system’sdesign in solar water heating for improving thermal efficiency. This work provides the basis for research exploiting high-temperature solar energy storage technologies and their applications in building sustainable energy systems.
subject: Articles
  topic: section
subject: renewable energy; solar thermal system; integrated solar collector; water storage tank; geometric design; thermal stratification; insulation cover.
  topic: keywords
creator: Narjes Hussain
creator: Karima Amori
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: 76
ending page: 96
doi: 10.22153/kej.2025.02.005

Enhancing the Surface Mechanical Properties of AL2618 Alloy Turbochargers Using a PLD Device with AI2O3
type: item
description: Pulsed laser deposition (PLD) has become a widely used technology for fabricating multicomponent thin films. In this research, nanomaterial Alfa-Al2O3 with a particle size of 30 ± 5 nm was deposited on the surface of AL-2618 alloy turbine blades. The microstructure, phase composition and the effects of a coating made from Alfa-Al2O3 nanomaterial were studied. PLD was employed to improve the mechanical performance of the aluminium alloy (AL-2618) used in turbocharger blades, primarily to reduce corrosion and erosion. To ensure high hardness and extended fatigue life, a 10 μm layer of aluminium dioxide was deposited on the surface of the aluminium alloy using PLD. The mechanical properties of the blade improved, as shown by hardness tests conducted on the samples. An energy-dispersive X-ray spectroscopy test was conducted before and after deposition. After depositing a nanolayer of Al2O3, the mechanical properties were enhanced. The hardness of the samples increased from 685 HLD to 781 HLD, and corrosion and erosion were reduced. The atomic percentage of oxygen deposited on the blade surface ranged from 0.6% to 0.8%, and the surface roughness (x) decreased from 49.5 µm to 22.8 µm because of the nanolayer applied via PLD.
subject: Articles
  topic: section
creator: Sarah S. Faraj
creator: Nabil H. Hadi
rights: Copyright (c) 2024 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2024-12-01
publication date: 2024-12-01
starting page: 89
ending page: 98
doi: 10.22153/kej.2024.07.004

Deep Learning Model for Prediction of Dementia Severity based on EEG Signals
type: item
description: This study aimed to determine variations in the electroencephalograms (EEGs) of 15 individuals who were diagnosed with mild cognitive impairment (MCI) following stroke, 5 individuals suffering from vascular dementia (VD) and 15 healthy normal control (NC) individuals who performed a working memory task. Conventional filters including notch and bandpass filters were utilised to remove noise from the EEG data. The proposed method comprises computing the estimates of the attention entropy (AttEn), bubble entropy (BubbEn) and symbolic dynamic entropy (SyDyEn) of univariate data sequence features. The long short-term memory (LSTM) deep learning neural network was used to automatically classify dementia severity through noninvasive EEG-based recordings. The LSTM classification result with AttEn exceeds an average of 88.9% than BubbEn and SyDyEn, with classification results of 69.2% and 77.7%, respectively. The analysis of the brain EEG-based dementia severity dataset suggests that AttEn could potentially serve as a biomarker for detecting dementia severity. AttEn can capture relevant patterns and features in the EEG data and may be indicative of the severity of dementia with LSTM RNN to differentiate patients with VD, patients with MCI and NC individuals.
subject: Articles
  topic: section
creator: Noor Kamal Al-Qazzaz
creator: Sawal Hamid Bin Mohd Ali
creator: Siti Anom Ahmad
rights: Copyright (c) 2024 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
date: 2024-12-01
publication date: 2024-12-01
starting page: 1
ending page: 12
doi: 10.22153/kej.2024.08.002

Deep-Learning-Based Mobile Application for Detecting COVID-19
type: item
description: Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated using precision, sensitivity, specificity, accuracy, and F-measure to classify CXR images into COVID-19, non-COVID-19 lung opacity, and normal control. Results showed a precision of 92.91%, sensitivity of 90.6, specificity of 96.45%, accuracy of 90.6%, and F-measure of 91.74% in COVID-19 detection. Indeed, the suggested MobileNetV2 deep-learning CNN model can improve classification performance by minimising the time required to collect per-image results for a mobile application.
subject: Articles
  topic: section
subject: Deep learning, CNN, MobileNet-V2, image processing, COVID-19, lung opacity, mobile display
  topic: keywords
creator: Noor Kamal Al-Qazzaz
creator: Alaa A. Aldoori
creator: Tabarak Emad Hussein
creator: Massarra Mohammed Mahdi
creator: Sawal Hamid Bin Mohd Ali
creator: Siti Anom Ahmad
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: 13
ending page: 27
doi: 10.22153/kej.2025.12.001

Violence Prediction Estimation in Surveillance Cameras Using CNN with GAMMA Correction
type: item
description: In recent years, the deployment of surveillance cameras has significantly increased to enhance security in public and private spaces. Numerous businesses continue to employ individuals to monitor these cameras. However, unusual and suspicious activities in the video feeds are often overlooked due to the potential for human error. Consequently, manual monitoring of security cameras can be time-consuming and inefficient. This study investigates the application of deep learning techniques, particularly convolutional neural networks (CNNs) and support vector machines (SVMs), to predict violence in surveillance video streams. The proposed CNN model is optimised through the utilisation of gamma correction as a preprocessing step to extract essential spatial features from video frames, significantly enhancing the accuracy of violence detection. This study leverages the real-time capabilities of surveillance data by utilising the RLV dataset, which comprises a range of violent and non-violent scenarios. The CNN–SVM hybrid model developed in this study achieved an impressive 99% accuracy, outperforming traditional methods and demonstrating strong spatial feature extraction capabilities. Furthermore, this study addresses the challenges of real-time video surveillance by ensuring scalability and practical applicability, providing a robust solution for enhancing security measures in public and private spaces.
subject: Articles
  topic: section
subject: anomaly detection, surveillance cameras, Convolutional Neural Network (CNN), Deep Learning, Violence detection, Support Vector Machine.
  topic: keywords
creator: Sara Aldulimy
creator: Yarub Alazzawi
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: 55
ending page: 65
doi: 10.22153/kej.2025.11.004