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
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.
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
Copyright (c) 2025 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
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.
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.
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.
Secure and Lightweight Cipher for Resource-constrained IoT Healthcare Applications using Snake Key Generation
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.
IoT healthcare; blockchain technology; cryptographic key generation; data security; NIST randomness
Examination the Corrosion Inhibition of Synthesized Azo-Schiff Bases Compounds from (P-Fluorophenyl)-1,3,4-Thiadiazoles for Carbon Steel Alloy in Acidic Medium
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.
Potentiodynamic, Polarisation, Thiadiazol, Aminoacetophenone, Hydroxy naphthalene, Carbon steel, Fourier Transform Infrared Spectroscopy, Dimethyl sulfoxide.
Path Loss Models for 5G Wireless Communication System Using mmWave Communication Channel
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.
MIMO, millimeter wave, path loss, ABG model, two-parameter CI model
Impact of Design Parameters in Thermal Solar Water Storage Tank Systems: (A Review)
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.
renewable energy; solar thermal system; integrated solar collector; water storage tank; geometric design; thermal stratification; insulation cover.
Enhancing the Surface Mechanical Properties of AL2618 Alloy Turbochargers Using a PLD Device with AI2O3
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.
Copyright (c) 2024 Al-Khwarizmi Engineering Journal https://creativecommons.org/licenses/by/4.0
Deep Learning Model for Prediction of Dementia Severity based on EEG Signals
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.
Deep-Learning-Based Mobile Application for Detecting COVID-19
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.
Deep learning, CNN, MobileNet-V2, image processing, COVID-19, lung opacity, mobile display
Violence Prediction Estimation in Surveillance Cameras Using CNN with GAMMA Correction
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.
anomaly detection, surveillance cameras, Convolutional Neural Network (CNN), Deep Learning, Violence detection, Support Vector Machine.
Improving Barcode Vision Scanning Process using a Drone-based Tracking PID Controller for Warehouse in Industry 4.0
Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference between the planned and the real barcode image dimensions, and making immediate changes to the drone position to improve the process of detecting the potential barcode. The aforementioned procedure is implemented on the DJI Tello drone to verify the practical performance of the methodology introduced in this study. Results showed that drones can achieve remarkable barcode scanning performance by incorporating sophisticated computer vision technologies into PID controllers. PID computer vision algorithms are capable of analysing visual data acquired from the drone’s cameras and retrieving barcode information under a variety of situations, such as the size of the barcode, location of the barcode and noise of the warehouse environment.
Drone; barcode scanning; vision camera; PID controller; warehouse; industry 4.0
Internet of Things Software-Defined Network Intrusion Detection Dataset: Inter- and Intra-Domain
Software-defined networks (SDNs) are extensively deployed in many network configurations. However, the development of new technology presents several vulnerabilities and risks that continue to pose challenges for manufacturers in addressing them. One of the primary obstacles encountered in deploying an intrusion detection system (IDS) is the absence of an openly accessible dataset, especially one obtained from SDN and SDN-based Internet of Things (IoT) networks. This work produces a comprehensive dataset to evaluate the effectiveness of anomaly-based IDSs in detecting inter- and intradomain attacks. The dataset comprises 86 features extracted from approximately 40 million records obtained from simulated SDN-based IoT networks captured within two flow profiles representing normal and 15 different attack types. In addition, the evaluation is demonstrated by employing six widely used machine learning and deep learning approaches for IDSs: decision tree classifiers, random forest classifiers, deep neural networks, K-nearest neighbours, Bernoulli naive Bayes, and logistic regression.
Dataset, intrusion detection system (IDS), Internet of Things (IoT), software-defined network (SDN), threat, attack vector, network intrusion dataset.
Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup
The limited mobility of lower limb amputees highlights the need for advancements in prosthetic control strategies to restore natural locomotion. This paper proposes an information fusion approach for gait phase recognition using surface electromyography (sEMG) and kinematics data. Time-domain (TD) features were extracted from the myoelectric data and three data-driven models, specifically Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), were compared in three different input conditions i.e. sEMG features, hip angle, and their fusion. Gait phase estimation results averaged from 40 healthy participants during normal walking with 10 strides per each demonstrated that the proposed fusion approach has consistently outperformed (p<0.0001) the other two conditions achieving a maximum accuracy of 85.48% with SVM. The findings suggest promising applications in prosthetic motion control and rehabilitative exoskeletons, highlighting the potential for improved user-driven strategies in lower limb prostheses.
Autonomous Path Planning and Obstacle Avoidance of a Wheeled Mobile Robot via Grey Wolf Optimisation
The aim of this study was to implement and compare obstacle avoidance for an autonomous wheeled mobile robot (WMR) via the grey wolf optimisation (GWO) algorithm and the artificial bee colony (ABC) algorithm. The study was conducted via three scenarios, each designed to test the performance of the algorithm under different conditions, considering fixed and moving circular obstacles in the surrounding environment. GWO was used to determine the most efficient, shortest and safest path for the WMR from the starting point to the target point. The results showed that the GWO outperformed the ABC. The GWO also enabled the WMR to avoid obstacles faster by 11.8%, 2.8% and 4.6% and with distances shorter by 1.42%, 2.2% and 1.97% for the three scenarios, respectively.
Bees Algorithm; Grey Wolf Optimisation; Wheeled Mobile Robot.
CuO/ZnO Nanocomposite Biosynthesis Approach Using Eucalyptus Leaves for Photodegradation of Tetracycline
In this study, the photocatalytic degradation of tetracycline (TC), which is responsible for water pollution, was investigated in an aqueous solution by using two types of nanoparticles (NPs) based on green synthesis of ZnO NPs and CuO NPs as photocatalysts. Eucalyptus plant extract was chosen as a good reductant and capping agent because of its cost-effectiveness, nontoxic characteristics and ease of usage. Different molar ratios of Cu:Zn (1:3, 1:6, 1:9 and 1:12) were prepared, and then these ratios were evaluated to select the effective ratio for removing TC. The 1:6 ratio demonstrated the best photocatalytic performance, degrading 72.15% of TC at the ZnO/CuO nanocomposite (NC) dose of 0.5 g/L, an initial concentration of 10 mg/L, pH 7, an agitation speed of 300 rpm, a temperature of 25 °C, UV intensities of 15 W/m2 and a contact time of 180 min. The synthesis of NCs was characterised using different analysis methods. CuO/ZnO characteristics were investigated using several analytical techniques, including FTIR, FESEM, EDAX, AFM, BET and zeta potential analysis. The NP’s structure, morphology, thermal behaviour, chemical composition and optical properties were analysed. The FESEM images verified that the (1:6) CuO/ZnO NPs calcined and not calcined, with sizes of 13.92 and 26.73 nm, respectively, were crystalline and spherical. The quantity of TC degradation by metal oxide NCs was investigated using UV–Vis spectroscopy. This method is sustainable and environmentally benign because it synthesises both NPs from plant sources. These hybrid nanoparticles may be used to treat other pharmaceuticals that are hazardous to reduce pollution in water.
A Comprehensive Survey of Push Recovery Techniques for Standing and Walking Bipedal Robots
Bipedal robots, which mimic human or animal motion, are expected to perform numerous tasks, such as delivering healthcare, conducting search and rescue missions in dangerous environments and serving industrial applications. Bipedal robots are required to interact with objects or people in their surroundings while performing their planned tasks. The main challenge faced by these robots is their ability to maintain balance in the presence of disturbances, such as external pushing forces applied to them and uneven terrains. Therefore, a push recovery control system that enables these robots to preserve stability while executing their intended tasks must be developed. This study investigates several push recovery control algorithms for bipedal robots operating in static (standing) or dynamic (walking) modes when faced with disturbances. The study further assesses the literature based on three factors: 1) the dynamic model used to represent the robot’s behaviour, 2) the control methods and 3) the required sensors. Moreover, this review paper emphasises the challenges that must be tackled in future research. These issues include the ability of bipedal robots to adapt rapidly to changing conditions in dynamic scenarios, the substantial energy consumption they require and the delays that arise from the complex and nonlinear structure of their movements. Hence, this research suggests some recommendations for effectively tackling these difficulties. 1) Sensory feedback approaches with machine learning algorithms should be employed to develop adaptable balance control systems that quickly learn from and react to different disturbances in real time. 2) Control algorithms that optimally balance stability and energy efficiency, such as predictive control algorithms that emulate the natural reflexes of humans, should be developed. 3) Hierarchical control systems should be used to partition the balance control problem into smaller stages, thus reducing the latency issues related to solving complex nonlinear equations.
Studying the Effect of Rotor’s Coefficients on a Y-shaped Tri-copter
Unmanned aerial vehicles (UAVs) are small yet highly capable aircraft widely used in many applications. UAV designs and shapes vary depending on their intended usage. Tri-copters, known for their agility, have three propellers. However, one of their main challenges is yawing, which occurs when the UAV rotates around its z-axis (yaw angle). This yawing issue is a result of the asymmetry in the number of propellers. Unlike quadcopters, which have an even number of propellers, the aerodynamic drag torque produced by the tri-copter’s propellers’ does not cancel out. In this paper, a Y-shaped tri-copter model is tested, modified and compared to address the yawing problem. Aiming to mitigate yawing, two propellers are set to rotate clockwise, whilst the third propeller rotates clockwise. In the first configuration, the force coefficient is set equally for all propellers. In the second configuration, the force coefficient of the counter-clockwise rotating propeller is doubled. The UAV model is controlled by six PIDs associated with feedback linearisation for both configurations—three PIDs for attitude control and three for altitude control. The PIDs are tuned using a genetic algorithm, and the system is simulated in MATLAB Simulink. The proposed configuration demonstrates lower integral time absolute error (ITAE) values, indicating improved UAV performance. The average ITAE values for the Y-shaped model are 0.1553 for the first case and 0.1017 for the second case. The second case shows remarkable tracking in the desired output, with no violation of design limitations (servo angles and motor speeds).
PID; genetic algorithm; UAV; thrust-vectoring; feedback-linearisation
Monitoring Alkalinity and Hardness During Treatment of Different Water Types
Alkalinity and hardness are crucial factors in water treatment procedures, such as electrocoagulation (EC) and traditional coagulation–flocculation. Two types of water were employed in this study to investigate the use of EC: river water and discarded reverse osmosis system water. With bipolar and monopolar aluminum electrodes, continuous flow tests were conducted to evaluate the effects of different parameters on the variations in alkalinity and hardness versus time. The examined factors were flow rate (600 and 1,000 L/h) and number of electrodes (2 and 4). Polarity reversal was also studied. The water passed through the plates in a zigzag pattern. Results showed that the hardness under all operating conditions decreased with time, and alkalinity exhibited an oscillating behaviour. The ideal hardness removal for treating river and concentrated water was obtained using four plates with polarity reversal and a flow rate of 1,000 L/h, which were equal to 37.5% and 33.3%, respectively.
Electromagnetic Scattering Properties and Characterisation of Sintered SiC Composite–Based Microwave Spectrum
Silicon carbide (SiC) is one of the most promising structural ceramic materials because of its excellent thermal and electromechanical properties. These properties are beneficial for CMC that enhances the performance of composites, especially those containing integrated nanoadditives. In this research, SiC composites were fabricated from SiC at three concentrations, ZnO and Si. The magnetic properties of the mixtures were tested through vibratory sample monitoring. The green samples were sintered in a sintering furnace at 1600 °C in a nitrogen environment. The obtained composites have been tested and characterised using different techniques, including X-ray diffraction, surface morphology using FESEM and network analyser, and the dielectric properties of the samples were tested. On the basis of the sintering environment, silicon nitride was detected in the composite because of the nitriding process along the composite. Weak magnetic and absorbing properties were calculated for all the SiC composites. The dielectric properties of a composite were considered high when the composite tended to be a reflector at a low frequency range and a transmitter as the frequency increased along the frequency band. Therefore, the composite exerted a potential effect on the applications where shielding properties required in microwave range to prevent wave interference.
Recent Tools of Software-Defined Networking Traffic Generation and Data Collection
Software-defined networking (SDN) has proven its superiority in addressing ordinary network problems, such as scalability, agility and security. This advantage of SDN comes because of its separation of the control plane from the data plane. Although many studies have focused on SDN management, monitoring, control and improving quality of service, only a few are focused on presenting what is used to generate traffic and collect data. The literature also lacks comparisons amongst the tools and methods used in this context. Therefore, this study introduces the recent tools used to simulate, generate and obtain traffic statistics from an SDN environment. In addition, the methods used in SDN data gathering are compared to explore the capability of each one and hence, determine the suitable environment for each method. The SDN testbed is simulated using Mininet software with tree topology and OpenFlow switches. An RYU controller was connected to control forwarding. The famous tools iperf3, Ping and python scripts are used to generate network datasets from selected devices in the network. Wireshark, the RYU application and the ovs-ofctl command are used to monitor and gather the dataset. Results show success in generating several types of network metrics to be used in the future for training machine or deep learning algorithms. Therefore, when generating data for the purpose of congestion control, iperf3 is the best tool, whilst Ping is useful when generating data for the purpose of detecting distributed denial-of-service attacks. RYU applications are the most suitable monitoring tool for obtaining all network details, such as the topology, characteristics and statistics of the components. Many obstacles and mistakes are also explored and listed to be prevented when researchers try to generate such datasets in their next scientific efforts.
SDN; OpenFlow1.3; Ryu controller; Mininet; iperf3; Wireshark; Python.