Middle East Journal of Pure and Applied Sciences (MEJPAS)
Middle East Journal of Pure and Applied Sciences (MEJPAS) (E-ISSN: 3062-343X) is an international, multi-disciplinary, peer-reviewed, double-blind, and open-access journal that enhances research in all fields of basic, engineering, and applied sciences through the publication of high-quality articles that describe significant and novel works and advance knowledge in a diversity of scientific fields. MEJPAS welcomes the submission of articles from all aspects of basic and applied science (Biology, Chemistry, Physics, Geology, and Mathematics), environmental science, agriculture, engineering, information technology, petroleum, and biomedical sciences, as well as from cross-disciplinary fields. YAYIMCI BİLGİLERİ (Publisher information) Yayımcı Adı: İKSAD GLOBAL YAYINCILIK Yayınlayan Kuruluş: İKSAD GLOBAL YAYINCILIK İmtiyaz Sahibinin Adı-Soyadı: MUSTAFA LATİF EMEK Sorumlu Yazı İşleri Müdürü: ETHEM İLHAN ŞAHİN Editör: Assoc.Prof.Dr. Llahm Omar Ben Dalla MEJPAS publishes one volume, four issues per year (each issue includes about 20-30 articles). Open Access Middle East Journal of Pure and Applied Sciences (MEJPAS) is committed to open access, and all content is freely available to all immediately after publication. Copyright Authors retain the copyright of their papers without restrictions. License MEJPAS applies the license of CC BY (a Creative Commons Attribution 4.0 International license). For more information, please follow the link https://creativecommons.org/licenses/by/4.0/. Publisher: IKSAD International Publishing House (Turkey)
Nano- and Micro- Engineering for Design of Smart Magnetic Metamaterials as Microwave-Absorbing
Electromagnetic (EM) wave-absorbing materials are of paramount importance in communication applications, radar stealth technology, and electromagnetic interference shielding. The design and analysis of a Smart Magnetic Metamaterial based on Split Ring Resonators (SRR) are explored in this study to achieve broadband and high absorption of Terahertz (THz) waves. Unlike conventional materials, metamaterials offer the ability to engineer their electromagnetic properties, specifically the complex permittivity and complex permeability, to achieve optimal Impedance Matching and energy dissipation. The unit cell of the absorber was designed and analyzed, and the surface current distribution was examined to determine the resonance mechanism. Simulation results indicated that high absorption is achieved by the proposed absorber in a broad band centered around 1.5‒1.6 THz. The effect of geometric variations, specifically the gap width (d), on the absorption characteristics and Group Delay was also analyzed. The potential of this design for developing ultra-broadband and efficient absorbing devices in the THz range is confirmed by the results.
Smart Magnetic Metamaterials, (SRR), EMW, HFSS., Absorption, Reflection Loss (RL) and Terahertz Range
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Performance Analysis of a Robust Face Recognition System Using the Fisherfaces Method Under Varying Environmental Conditions
Face recognition remains a fundamental biometric technique for identification, yet it continues to face significant challenges regarding illumination and expression variations (Belhumeur et al., 1997). This study presents a face recognition system based on the Fisherfaces method, which leverages Linear Discriminant Analysis (LDA) to maximize class separability (Martinez & Kak, 2001). The proposed methodology integrates image preprocessing steps, including grayscale conversion and normalization, with a dual-stage feature extraction process involving Principal Component Analysis (PCA) and LDA. Classification is performed using a Nearest Neighbor (NN) approach to determine identity. Experimental evaluations on a standard dataset demonstrate an overall recognition accuracy of 90%, with performance levels ranging from 85% to 95%. These results indicate that the Fisherfaces framework remains robust under diverse conditions (Zhao et al., 2003), offering a reliable solution for real-world applications in security and surveillance.
Face Recognition, Fisherfaces, Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Pattern Recognition, Biometrics
Response Of Mungbean to Gibberellic Acid (GA₃) And Nitrogen Application with Reference to Yield and Protein Content
An experiment was carried out in the field of Sher-e-Bangla Agricultural University, Dhaka to study the impact of GA₃ and nitrogen on growth, yield attributes and protein content of mungbean. The experiment was conducted using as treatments three levels of GA₃ (0, 50 and 100 ppm) combined with four nitrogen doses (0, 22, 44 and 66 kg ha⁻¹ of urea), distributed in RCBD with three replications. Yield and quality parameters were largely affected by GA₃ treatment. 50 ppm GA₃ application generated maximum number of pods per plant (107.52), seeds pod⁻¹ (9.97), chlorophyll content (52.75%), seed yield (2.16 t ha⁻¹), stover yield (1.05 t ha⁻¹), nitrogen content (4.11%) and protein content (25.72%) while minimum values were recorded in control treatment. Nitrogen was also effective in enhancing plant growth and yield. Plots receiving 44 kg urea ha⁻¹ gave significantly the maximum pods per plant (108.94), seeds per pod (10.14), 1000- seed weight (47.22 g), seed yield (2.16 t ha⁻¹) and stover yield (1.08 t ha⁻¹) while minimum value was recorded in control plots followed by all other treatments for these attributes in between the range of highest and lowest values respectively of these characters among the treatment combinations applied under study.. The application of 50 ppm GA₃ in combination with 44 kg urea ha⁻¹ (G₁N₂) showed the best effect (i.e., significantly superior), which produced the maximum number of pods per plant (132.27), seed per pod (11.30), thousand-seed weight (47.92 g), seed yield (2.38 t ha⁻¹), stover yield (1..28 t ha⁻¹) and both nitrogen content of seeds (4.20%) and protein content (26..25%). Hence it is recommended that 50 ppm GA₃ along with 44 kg urea ha⁻¹ is the most effective treatment for obtaining higher mungbean yield and protein content.
An experimental study to convert a domestic refrigerator to produce cold water using R134a as a working fluid
Abstract: This study designed and constructed a simple vapor compression refrigeration system using R134a as the working fluid. The system's objective was to produce chilled water by immersing an evaporator coil into a water tank. The experiment was conducted under normal atmospheric conditions in a laboratory at the Faculty of Mechanical Engineering Technology in Benghazi, Libya. System performance was monitored by measuring pressure and temperature at the inlets and outlets of the primary components. The enthalpy changes of R134a were calculated to analyze its phase changes throughout the cycle, which were plotted on a P-h diagram. The Coefficient of Performance (COP) was determined for multiple experimental runs. The maximum recorded COP for the system was 2.4.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Designing a Hybrid Control System to Operate Two Pumps Alternately Using Manual and Automatic Control Techniques
Traditional pump control systems face challenges such as increased mechanical stress, high failure rates, and wasted energy due to the continuous operation of a single pump. To address these issues, this paper aims to design and evaluate a hybrid control system that enables alternating operation of two pumps, allowing the user to switch between manual and automatic modes. The proposed system relies on conventional electrical components like contactors, time delay relays (timers), and protection circuit breakers, making it a low-cost, easy-to-maintain solution that does not require programmable logic controllers (PLCs). A practical model of the circuit was built and tested under various conditions, including independent manual operation for each pump and automatic alternating operation based on a predefined time interval. The evaluation results indicate that the system demonstrates a high level of operational flexibility and reliability, effectively ensuring alternating pump operation while minimizing energy consumption and mechanical wear. The design also provides a high degree of electrical safety through the use of appropriate protection devices. The study recommends adopting this system for small- and medium-scale projects that require high operational efficiency and low costs, with the potential for future expansion toward intelligent digital solutions if needed.
Optimization of PID Parameters Based on Ant Colony Optimization Algorithm for Ball and Beam System
This paper investigates the control of the inherently unstable ball and beam system, a canonical benchmark problem in control engineering known for its nonlinear dynamics and challenging control requirements. The study's primary objective is to develop and rigorously compare different control strategies for achieving precise ball positioning on the beam. The research begins with the derivation of both linear and nonlinear mathematical models of the ball and beam system. These models incorporate the intricate dynamics of the DC servomotor, responsible for tilting the beam, and the coupled mechanical dynamics governing the ball's movement along the beam's surface. The core of the research focuses on evaluating the performance of a Proportional-Integral-Derivative (PID) controller, a widely used control strategy, tuned using three distinct methods: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and a traditional trial-and-error approach. Extensive simulations conducted within the MATLAB/Simulink environment allow for a detailed comparison of these tuning methods, using key performance indicators such as settling time, rise time, overshoot, and steady-state error. The findings contribute significantly to the broader understanding of optimal control strategies for unstable nonlinear systems and offer valuable insights into the relative strengths and weaknesses of different optimization algorithms for efficient PID controller parameter tuning. The results provide a practical guide for selecting appropriate tuning methods based on specific performance requirements and computational constraints.
Towards Safer Driving: A Review of Real-Time Drowsiness and Hypoglycemia Detection Using Embedded Machine Learning and IoT-Based Alerts
Traffic accidents often result in both human casualties and economic losses worldwide. Among the major causes of these accidents are driver drowsiness and hypoglycemia. In recent years, artificial intelligence technologies have significantly advanced and been applied across many domains, including road safety, particularly in detecting abnormal driver states such as drowsiness or low blood sugar. Computer vision techniques based on cameras have been used to monitor facial features such as blink rate, yawning, and head movement. Additionally, physiological sensors have been integrated into these systems using various biosensors. IoT technologies have also been employed to send alerts for remote driver monitoring, enhancing system reliability. This study aims to provide a comprehensive review of the technologies used to detect drowsiness and hypoglycemia in drivers and to highlight the progress made in this area. The goal is to emphasize the importance of research toward developing integrated systems that can simultaneously detect both conditions. The study concludes that integrating computer vision, biosensors, and machine learning algorithms can significantly contribute to the development of high-accuracy systems that improve road safety.
Drowsiness and Hypoglycemia Detection System
Data Fusion Techniques for Multimodal Models in Medicine: A Review
The increasing availability of diverse data sources in medicine necessitates sophisticated data fusion techniques for building comprehensive and accurate models. This review paper explores the landscape of data fusion techniques employed in multimodal medical models. These techniques are categorized based on the level of fusion: early, intermediate, and late. Prominent methodologies, including deep learning-based approaches, kernel methods, and probabilistic graphical models, are discussed along with their applications in medical image analysis, clinical decision support, and personalized medicine. Challenges associated with multimodal data fusion in medicine and potential future research directions are also outlined.
Evaluating the Effect of Weather Patterns on Solar PV Using Historical Data in Libya
Solar energy potential in Libya is high due to abundant sunlight, but weather variability (temperature, humidity, wind, cloud cover, dust) significantly affects photovoltaic (PV) output. We review studies on environmental effects (e.g. high temperatures and dust reduce efficiency) and present a modeling analysis using public climate datasets (ERA5, NASA/POWER) to quantify these impacts. Using simulated Libyan weather and a fixed-tilt PV model, we train regression and ensemble models (Linear Regression, Random Forest) to predict PV power from irradiance, ambient temperature, humidity, and wind speed. Correlation analysis (Table 1) confirms that irradiance is the dominant factor (correlation ~0.997), while temperature and humidity have weaker but non-negligible effects. Our models achieve high accuracy (Table 2; e.g. R²≈0.995–1.000) in reproducing PV output. These results underscore that weather variability must be accounted for in solar planning for Libya. In particular, high desert temperatures and dust storms can substantially reduce generation, impacting energy security. We discuss implications for grid integration and PV system design in Libya’s renewable energy strategy.
Solar Forecasting, Irradiance and PV Output
Digital Transformation in Business Administration: Analyzing the Impact of Emerging Technologies on Organizational Performance
Digital transformation (DT) is the integration of digital technologies into all areas of business which is reshaping organizational processes and outcomes globally. This paper examines how emerging technologies (cloud, artificial intelligence, Internet of Things, etc.) affect performance across major industries (manufacturing, finance, healthcare, retail). We adopt a data-driven, case study approach, synthesizing evidence from academic journals, industry reports, and global statistics. Key findings include: (1) digitization is widespread but uneven by sector (knowledge-intensive industries lead while others lag); (2) top-performing firms with advanced digital capabilities outperform peers (e.g. AI “leaders” realize ~1.5× revenue growth and 1.4× ROI); (3) many organizations under-realize expected gains (only ~31% of projected revenue lift is captured on average). We illustrate these effects via sector-level figures (e-commerce growth, adoption rates) and real-world cases (e.g. IoT in manufacturing). The evidence suggests that digital investments can greatly enhance productivity and innovation, but success depends on strategy, culture, and measurement. In conclusion, emerging technologies present major performance upside globally, provided firms adopt a holistic transformation framework and metrics to realize value.
Urinary Tract Infection Afield Study in Bani Walid General Hospital
This research aims to study urinary tract infections (UTIs), which are commonly caused by the presence of bacteria in the urinary system. Normally, most bacteria are expelled during urination; however, some may remain within the urinary tract, leading to infection. UTIs can affect various parts of the urinary system, including the kidneys, ureters, bladder, and urethra. Urine flows from the kidneys through the ureters into the bladder and is then expelled through the urethra. This field study was conducted at Bani Walid General Hospital, where 500 urine samples were collected from individuals visiting the hospital, including both males and females as well as children. The samples were analyzed at the Ibn Sina Clinic Laboratory and the Bani Walid General Hospital Laboratory during the period from June 1, 2023, to December 30, 2023. The results showed that the infection rate varied by gender, with a higher prevalence among females at 60% (300 cases), compared to 40% (200 cases) among males. Keywords: Urinary tract infection, urinary system, UTI, Bani Walid
Residual Gradient Fuzzy Actor-Critic Learning-Based Control for Two-Tank Level System
The paper presents a novel application of the residual gradient fuzzy actor-critic learning (RGFACL) algorithm to nonlinear level control of a two-tank system. In contrast to traditional fuzzy actor-critic methods using direct gradient methods, which are commonly susceptible to instability and convergence issues, the RGFACL algorithm presented in this work employs a residual gradient formulation to ensure a more precise and stable learning process. Moreover, the algorithm simultaneously adaptively adjusts the premise (input) and consequent (output) parameters of the fuzzy inference systems, increasing the expressiveness and flexibility of the control strategy. To the best of our knowledge, this is the first implementation of the RGFACL approach on a two-tank benchmark system. The simulation results demonstrate that the RGFACL algorithm achieved improved transient response, reduced overshoot, and enhanced robustness compared to the traditional PID controller. The RGFACL algorithm successfully handled abrupt setpoint changes, its ability to adjust within nonlinear, time-varying operating conditions clear. The results confirm the efficacy of employing the RGFACL learning algorithm to the nonlinear and complex environments.
The Role of Green Technologies in Shaping Sustainable Economic Growth
This review examines how green technologies (including renewable energy, sustainable transport, energy efficiency, circular economy practices, and green construction) contribute to sustainable economic growth across regions. We synthesize findings from peer-reviewed studies (2018–2024), international agency reports (World Bank, IEA, IRENA, OECD, etc.), and data sets, with attention to G7, BRICS, EU, and developing countries. Empirical evidence generally shows that expanding green tech spurs growth and job creation (e.g. China’s clean-energy boom drove ~40% of GDP growth in 2023), though some short-run trade-offs have been noted in select contexts. Key quantitative results include elasticities from panel regressions and input–output analyses linking renewable penetration to GDP, as well as global statistics (e.g. 13.7 million renewable energy jobs worldwide in 2022). We also identify barriers (financing gaps, policy uncertainty, skill shortages) and enablers (innovation policy, finance mechanisms, training) of the green transition. Case examples (China’s clean-energy surge, EU Green Deal, urban clean construction) illustrate how targeted policies amplify benefits. The discussion highlights that green tech adoption can decouple growth from pollution if supported by coherent strategies. Policy implications emphasize scaled investment, incentives for green R&D, workforce development, and international cooperation. The paper provides a foundation for understanding how green innovation drives inclusive growth.
Experimental Investigation of Pavement Deterioration Using Theoretical Models and Computational Methods
This study comprehensively compares multiple approaches to modeling flexible pavement deterioration, including mechanistic-empirical models, stochastic (Markov) models, physics-based fracture mechanics, finite-element simulations, and data-driven machine learning (ML) methods. Using published performance data (e.g. the Long-Term Pavement Performance (LTPP) database) and synthetic experiments, we evaluate model predictions of surface distress (cracking, rutting, roughness). Mechanistic–empirical (M-E) and fracture-based models provide physical insight into damage processes, while Markov chains capture condition transitions probabilistically. In parallel, ML algorithms (Random Forest, CatBoost, Artificial Neural Networks) are trained on the same data; hybrid schemes use FEA-generated stress and deflection outputs as ML inputs to enhance accuracy. Our results (summarized in Table 1) show that advanced ML and hybrid models generally outperform classical regression and Markov models in predictive accuracy (higher R², lower RMSE). For example, a CatBoost-based hybrid model achieved R²≈0.96 on a sample dataset versus R²≈0.88 for linear regression. These findings imply that integrating physical simulation data with data-driven models yields the best performance forecasts. Such improved models can guide optimized maintenance planning by more reliably identifying when and where interventions (e.g. resurfacing, repairs) will be most effective.
Neuroscience of Addiction Molecular Mechanisms and Potential Therapeutic Targets
Addiction is a chronic brain disorder characterized by compulsive substance use despite adverse consequences. This paper explores the molecular mechanisms underlying addiction, focusing on neurotransmitter systems such as dopamine, glutamate, and the endocannabinoid system, as well as genetic and epigenetic changes that reinforce addictive behaviors. Various therapeutic targets, including pharmacological interventions, gene therapy, epigenetic modulation, neuroimmune modulators, and emerging technologies like CRISPR-Cas9, are discussed as potential pathways to treat addiction. The paper emphasizes the need for personalized, multi-faceted approaches to effectively address the complexities of addiction and highlights the challenges in translating scientific advances into real-world treatment options. By integrating innovative strategies and targeting the core molecular drivers of addiction, this research offers a comprehensive understanding of potential interventions that could pave the way for more effective and sustainable recovery solutions.
The Impact of 3D Printing on Electrical Engineering: From Prototyping to Mass Production
This research paper explores the transformative impact of 3D printing on the field of electrical engineering, from its initial role in rapid prototyping to its evolving application in mass production. As additive manufacturing technologies advance, 3D printing has emerged as a powerful tool for designing, testing, and producing complex electrical components, including printed circuit boards (PCBs), connectors, sensors, and customized housings. Key benefits such as cost reduction, increased design flexibility, and material efficiency make 3D printing a game-changer for electrical engineers. However, challenges remain in scaling the technology for mass production, particularly concerning production speed, material performance, and quality control. The paper highlights emerging trends, such as multi-material printing, 3D-printed electronics, and AI-driven optimization, which promise to further integrate 3D printing into mainstream electrical engineering. The environmental and economic implications are also discussed, positioning 3D printing as a sustainable and cost-effective alternative to traditional manufacturing methods. Through an analysis of current and future applications, this research concludes that 3D printing will play an increasingly significant role in electrical engineering, driving innovation and reshaping production models.
Emerging Trends in Quantum Sensors: Applications in Defense and Communication
This research paper explores the emerging role of quantum sensors in transforming critical sectors such as defense and communication. Quantum sensors, leveraging the unique principles of quantum mechanics like entanglement, superposition, and tunneling, are enabling unprecedented precision, security, and efficiency in military and communication networks. Real-world applications of quantum sensors in Quantum Key Distribution (QKD), precision navigation, and stealth detection are highlighted through case studies from the United States, China, and Europe. The paper also addresses the technological challenges in scaling quantum sensor deployment, such as sensitivity, cost, and integration with existing systems, as well as the ethical concerns regarding the militarization of quantum technologies and the potential for triggering new arms races. Future trends such as the miniaturization of quantum sensors and their integration with Artificial Intelligence (AI) for real-time decision-making are explored. The research concludes by underscoring the need for international cooperation to manage the development and application of quantum technologies in a way that enhances security without escalating global tensions.
Power Electronics in Renewable Energy Systems Enhancing Efficiency and Reliability
The integration of renewable energy systems into the global power grid is essential for achieving a sustainable, low-carbon future. However, the intermittent and variable nature of renewable energy sources such as solar and wind presents significant challenges to maintaining efficiency and reliability. Power electronics, which involves the conversion, control, and conditioning of electric power, plays a critical role in addressing these challenges. By enabling efficient energy conversion and stabilizing energy output, power electronics enhances both the performance and dependability of renewable energy systems. This paper explores the vital role of power electronics in improving the efficiency and reliability of renewable energy systems, discusses current advancements, and highlights future opportunities for innovation in this field. The findings underscore the importance of continued research and development in power electronics to support the broader adoption and success of renewable energy technologies.
The Role of Edge Computing in Revolutionizing Cloud-Dependent IT Systems
Edge computing is transforming how we manage cloud-dependent IT systems by addressing key challenges like latency, bandwidth limitations, and security concerns. Unlike traditional cloud computing, which centralizes data processing, edge computing brings it closer to where data is generated. This shift enables faster processing, more efficient use of network resources, and enhanced security by reducing the amount of sensitive data transmitted over long distances. In this paper, we explore the evolution of cloud computing and the growing need for innovation as cloud adoption increases. We delve into the synergy between edge computing and emerging technologies like 5G, AI, and machine learning, which are further enhancing its potential. Case studies in industrial IoT, healthcare, and smart cities demonstrate how edge computing is already making a significant impact. However, challenges such as infrastructure complexity, data management, and security must be addressed to fully realize its benefits. Edge computing is not just a complementary technology but a revolutionary force reshaping the future of IT systems. As it continues to evolve, it promises to make our systems smarter, faster, and more secure.