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.
Al-Khwarizmi College of Engineering / University of Baghdad
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Smart Energy Management System Based on Embedded Wireless Communication
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.
Embedded wireless communication; Smart meters; Smart energy management systems; IoT; Machine learning for energy optimization
Copyright (c) 2026 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
Performance Evaluation and Optimisation of Spectrum Management in Communication Systems by GA
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.
Cognitive Radio; Crossover; Detection Rate; Dynamic Spectrum Access; Genetic Algorithm; Optimisation; Spectrum Sensing
Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
YOLOv8-Edge: High-Accuracy Real-Time Social Distancing Monitoring on Resource-Constrained Embedded Systems
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.
YOLOv8 algorithm; Deep learning; Social distancing; Object detection; Computer vision
Analysing the Effects of Switching, Conduction and Thermal Losses on IGBT Performance in Boost DC–DC Converters for Photovoltaic Systems
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.
IGBT losses; step-up DC–DC converter; photovoltaic systems; conduction losses; switching losses; thermal control; efficiency optimisation; continuous conduction mode (CCM)
Rabbit-256 Optimisation for Secure Blockchain Hashing in IoT-Healthcare Data
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).
healthcare; hashing; medical internet of things; security; cloud computing; blockchain
Vendor Performance Monitoring in Automotive Manufacturing Under Localization Strategy
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.
Vendor performance; Automotive manufacturing; Supply chain management; Localization strategy; Supplier evaluation
Artificial Intelligence-Based Control Strategies for Flow Rate Control of Fluids and Gases in Energy and Process Systems: A Systematic Review
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.
Control system; Flow rate; Adaptive control; Fluid; Artificial intelligence
Supply Chain Resilience in the Context of Global Production and Logistics
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.
supply chain resilience; production planning and control; hybrid resilience-efficiency models; cyber-resilience frameworks; multi-industry strategies
Pulse Wave Speed Estimation: A Comparative Study of the PU-Loop Technique versus Tensile and Compliance Testing on Synthetic Elastic Tubes
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.
Bramwell–Hill equation; Compliance testing; Moens–Korteweg equation; PU-loop; Tensile testing.
Preparation, Characterization, and Evaluation of Electrospun Copolymer Nanofiber Membranes for Kerosene Removal from Aqueous Solution
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.
Polyacrylonitrile; Polysulfone; Copolymer; Electrospinning; Nanofiber membranes; Oily wastewater
Effect of Fibre Loadings on the Tensile Properties of PALF/PHBV Composite: A Two- Parameter Weibull Analysis
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 the dependable design of sustainable PHBV-based composites.
tensile strength; PALF; fibre loadings; Weibull distribution; PHBV polymer
Sensitivity Analysis of the Integrated CRITIC-TOPSIS Method for Selecting Optimal Natural Fibre–Reinforced Composites for Toe Caps in Safety Footwear
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.
Natural fibre composite; Multi-criteria decision Making (MCDM); CRITIC; TOPSIS; Material selection
Design of a Compact Broadband Metamaterial Absorber with Wide-Angle Reception and Polarization-Insensitive for Electromagnetic Energy Harvesting in Wireless Sensor Network Applications
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.
Broadband; Metamaterial absorber; Polarization-insensitive; RF electromagnetic energy harvesting; Wide-angle reception; Wireless sensor network (WSN)
Optimization of Phenol Adsorption Using Activated Carbon Derived from Spent Tea Leaves in a Packed-bed Column
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.
Breakthrough curve; Packed-bed adsorber; Response surface methodology; Regeneration studies; Spend tea leaves; Wastewater
Analytical and Mechanical Study of Transtibial Osseo-Integrated Prostheses Implant
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.
Keywords: Implants; osseointegration; prosthetics; bone attachment; Ti–13Nb–13Zr alloy.
Thermal Comfort and Internal Conditions Testing of Standard Relief Tents in cold Climates
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.
temporary shelter; thermal comfort; CBE MRT; CBE Thermal Comfort tools; PMV; PPD
Real-Time Adaptive Traffic Signal Control with YOLOv10 and Image Processing
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.
Intelligent traffic lights control; Deep learning; Image processing; Traffic flow optimisation; Smart cities.
Synthesis and Characterization of Flash Graphene for Use in Remediation of Contaminants in Iraqi Groundwater Using Batch Adsorption Techniques
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.
adsorption; cation; flash graphene; BPAc; groundwater; contaminant; synthesize.
Impact of the Number of Reflecting Elements and Positioning of the Reconfigurable STAR-Passive-ORIS in MIMO FSO Systems
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.
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).
Autonomous Vehicle Steering-based Feedback Linearization and Sliding Mode Control
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.
Feedback Linearization, Robust control , SMC.
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.
Biodiesel; Castor oil; Alkaline transesterification; Renewable energy; and non-edible oil.
Effect of Heat Treatment on the Microstructure, Impact Toughness and Hardness of High-Chromium White Cast Iron with Added Niobium and Manganese Minerals
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.
Wear; impact toughness; hardness; grinding; high-chromium white cast iron; cracking
K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm
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.
AdaBoost; ensemble learning; feature selection; genetic algorithm; K-means clustering; machine learning; phishing detection
Analyzing Geothermal Heat Exchangers Using Comsol Non-Isothermal Pipe Flow Model
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.
geothermal energy, sandy soil, earth heat exchanger, COMSOL, thermal analysis
Removal of Methylene Blue from Wastewater Using AL Haji Plant as a Low-Cost, Eco-Friendly Adsorbent
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.
Al Haji plant; Batch sorption; Kinetic models; Methylene blue; Thermodynamic Parameter.
Preparation and Characterisation of Activated Carbon from Pomegranate Waste Using Chemical with Microwave Activation
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.
Application of Aqueous Two-Phase System in the Extraction of Invertase Enzyme from Potato Tubers Using PEG8000/Potassium Phosphate
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.
Aqueous two-phase system, Enzymes, plant source, Potato tuber.
Development and Implementation of an Automated Production Line Training System
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.
Automated production line; Programmable logic controller; Training system; SCADA .
Improving the Performance of Steering by Wire Using a Model Predictive Controller Enhanced with Particle Swarm Optimisation
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.
Steering by wire, Vehicle, Model predictive controller, Particle swarm optimisation, Controller.
Object Dimension and Location Determination in Gantry Robots and Conveyor Systems: A Review
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.
Gantry robot; Dimensions; Location; Sensors; Vision; Conveyor system