Gharyan University Journal of Engineering Science (GUJES)
Gharyan University Journal of Engineering Science (GUJES) is a peer-reviewed, open access journal that publishes original research articles as well as review articles every 6 months. The aim of GUJES is to provide information that enhances performance of engineering and technology science and strengthening the crooks to make smarter decisions, provide better care and from time to time make innovative discoveries that enhance the limitation associated with knowledge of advancement.
Uplink Performance Enhancement for MU-MIMO Wireless Systems with STBC and Regularized ZF Equalization
This paper presents a robust performance study of uplink Multi-User Multiple-Input Multiple-Output (MU-MIMO) wireless communication system with Space-Time Block Coding (STBC) under Rayleigh fading. The model adopts QPSK modulation and numerically stable Zero-Forcing (ZF) equalizer with regularization to cancel inter-user interference and for better numerical stability. The system is analysed using both BER, user and antenna connection capacity. MATLAB simulations demonstrate that increasing the number of receive antennas significantly improves both BER and capacity. Moreover, the proposed regularized (ZF) detection method outperforms conventional approaches, offering improved robustness and reduced complexity. This study showed the effectiveness of the proposed solution and performance of next generation wireless networks through comparative analysis with other prior MU-MIMO schemes.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
تكامل الذكاء الاصطناعي والمشاركة المجتمعية: إطار عمل هجين لتعزيز مرونة المدن الهشة (تعز، اليمن) في مواجهة الفيضانات
تناقش هذه الدراسة الفيضانات الحضرية في مدينة تعز بوصفها نتاج تفاعل المناخ القاسي مع الصراع وضعف الإدارة والفقر المتجذر. تعاني مدينة تعز من شحة بيانات وتجزئة مؤسسية، ما يجعل الأساليب التقليدية غير فعالة اجتماعيا واقتصاديا. وبالتالي, تسعى الدراسة الى معالجة فجوات معرفية وتطبيقية عبر إطار متكامل يجمع التحليل التنبؤي مع تدخلات مجتمعية ميدانية. وقد اعتمدت المنهجية على تقدير مخاطر الفيضانات حتى 2030 باستخدام نموذج SCS-CN، ونماذج تعلم آلي تشمل Random forest و ARIMA مع تحليل جغرافي مكاني عبر منصة Google earth engine وبياناتSentinal-1 للفترة 2015 إلى 2024. أظهرت النتائج أن 40% من مساحة تعز قد تواجه مخاطر عالية بحلول 2050، مع زيادة شدة الأمطار بنسبة 20 إلى 30%. خفضت مبادرات مجتمعية لإصلاح الصرف وتكرار الفيضانات 35%، وحسنت مستشعرات IoT زمن الاستجابة 60%. وتؤكد الدراسة أهمية نماذج قابلة للتوسع تجمع التقنية منخفضة التكلفة مع الحوكمة التشاركية، وتدعو لإعادة توجيه التمويل الدولي لدعم هذه المقاربات القائمة على البيانات الفعلية.
Reliability Enhancement and Grid Performance of Regional Network through Strategic Load Expansion Planning with Photovoltaic Integration
This paper presents a comprehensive analysis of load expansion planning through the integration of photovoltaic systems at the Bir Alganame 220/30 kV substation. An electrical planning system serves as a crucial tool for designing electrical systems that effectively meet evolving energy demands. The study investigates the impact of load expansion on hybrid photovoltaic system design, focusing on system reliability and total project cost as primary design objectives. The research methodology encompasses a detailed load flow study conducted in 2024, coupled with load forecasting for 5 and 10-year horizons at an 8% load increase rate. The investigation includes analyzing the optimal parameters of the PV system integration with the existing grid to ensure proper functionality. Additionally, the study addresses the probabilistic scenario set of load demand and natural characteristics of renewable energy, which is crucial for power system planning studies. The research emphasizes the importance of proper transmission system network development to reliably and securely evacuate the generated power to meet future domestic load demand. Through simulation and analysis, this study demonstrates an integrated approach to improving power system performance while considering geographical and environmental factors affecting PV output. The findings provide valuable insights into optimal electrical network planning procedures, incorporating both long and short-term perspectives through PV system integration.
التحكم في مقومات تعديل عرض النبضة ثلاثية الطور باستخدام تقنية الهيستيريسيس
أدى التطور الكبير في إلكترونيات القدرة، واستعمالاتها الواسعة في المجالات الصناعية المختلفة إلى البحث عن تقنيات تحكم تساعد في تحسين كفاءة الطاقة لشبكات الكهرباء المختلفة. تعد مقومات التحكم في عرض النبضةPulse-width modulation (PWM) rectifier من أكثر المقومات استعمالا خصوصا في مجالات الطاقات المتجددة نظرا للمزايا التي يوفرها هذا النوع من المقومات، متمثلة في إمكانية التحكم في الجهد المستمر الخارج من المقوم، وتقليل التوافقيات (Harmonics) في تيار المصدر، بالإضافة إلى تحسين كفاءة الطاقة بالرفع من معامل القدرة Power factor (PF) إلى ما يقارب الواحد. تقدم هذه الورقة المفاهيم الأساسية لعمل مقومات التحكم في عرض النبضة ثلاثية الطور ويتعرض البحث بشيء من التفصيل إلى إحدى تقنيات التحكم الشائعة والمعروفة بتقنية التحكم في تيار الهيستيريس (Hysteresis current control). لغرض تصميم التحكم بشكل مناسب، تم التطرق إلى النموذج الرياضي للمقوم، والذي استُخدِم فيما بعد لتصميم التحكم باستخدام تقنية الهيستيريس. تم تنفيذ واختبار تقنية التحكم المقترحة باستخدام برنامج المحاكاة (Matlab/SIMULINK)، ومن ثم عرض النتائج لتقييم متانة التقنية المقترحة في الحصول على نتائج مميزة تحقق أهداف الورقة المتمثلة في: التحكم في الجهد الخارج-تقليل التوافقيات في تيار المصدر-الحصول على معامل قدرة عالي يقارب الواحد
Enhancing Support Vector Machine Performance Using Particle Swarm Optimization for Arabic Text Classification
Arabic is a very complex language, stemming from its high morphological richness, high linguistic complexity, and inadequate sizes of available quality labeled resources. In this paper, we attempt to address such obstacles by proposing a novel hybrid model that combines SVM and PSO for improving Arabic text classification. The efficacy of SVMs for classification is known to be good, their performance depends on hyperparameter settings. The data has been classified using SVM with optimized parameters (C, Gamma, Kernel type and feature subset) on the basis of PSO technique which is a strong metaheuristic algorithm. The experimental results of the proposed SVM-PSO were evaluated with a corpus composed from different on line sources in Arabic, these representing multiple lineage dealing with various categories such as news (sports, politics), history, geography and IT. Experiments show that the proposed method not only results in significantly better performance than compared methods with a large advantage, but also high classification accuracy (99.2%). The model was also found to have substantially greater precision (99.1%), recall (99.0%), and F-Score (99.1%) as compared to traditional machine learning classifiers, namely standard SVM (92%), Naive Bayes (88%) and Random Forest (94%). This research showed that combining metaheuristic search for parameter tuning, along with classical machine learning can result robust classifier to process Arabic text as one of those state-of-the-art ones in the fair field of NLP.
Arabic Text Classification, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Natural Language Processing (NLP), Hyperparameters Optimization.
Copyright (c) 2026 Gharyan University Journal of Engineering Science (GUJES) https://creativecommons.org/licenses/by-nc/4.0
Enhancing Vaccine Adverse Event Detection on Social Media through LLM-Driven Synthetic Data Augmentation
This paper evaluates the performance impact of synthetic data augmentation on the detection of personally experienced vaccine reactions in social media posts. Our study uses the UoT team's submission for Task 6 of the 10th Social Media Mining for Health (#SMM4H) Shared Tasks as a foundation. By establishing a baseline through the fine-tuning six Large Language Models (LLMs), we analyze how augmenting the training set with synthetically generated examples influences classification metrics. Our experiment shows that synthetic augmentation leads to substantial performance improvements across all models with an additional benefit to small models.
LLMs, Vaccine Adverse Event Reporting, Synthetic Augmentation
تقييم مخاطر إدارة سلسلة الإمداد وأثرها على مستوي الأداء التشغيلي: دراسة حالةبشركة الواحة للنفط
تهدف الدراسة إلى تقييم مخاطر إدارة سلسلة الإمداد وأثرها على مستوى الأداء التشغيلي بشركة الواحة للنفط. ولتحقيق أهداف الدراسة، تم الاعتماد على المنهج الوصفي التحليلي، واستخدام الاستبانة كأداة رئيسة لجمع البيانات,حيث تمت الدراسة لمجتمع موظفي إدارة المواد بالشركة، وتم اختيار عينة عشوائية بسيطة بلغ حجمها (93) مفردة،استُرجع منها (78) استبانه صالحة للتحليل بنسبة (84%). جرى تحليل البيانات باستخدام الأساليب الإحصائية الوصفية المناسبة لطبيعتها. توصلت الدراسة إلى أن أبرزمخاطرسلسلة الإمداد تتمثل في غياب خطة إستراتيجية للتدريب في مجال إدارة مخاطر سلسلة الإمداد، وضعفها تماما لإدارة بتطويروتحسين أداء السلسلة، إضافة إلى محدودية تطبيق الإجراءات الجزائية على الموردين وضعف التواصل معهم. كما أظهرت نتائج تقييم الأداء التشغيلي وجود بطء في الاستجابة لتذبذب أسعار المنتج، وعدم إدراج تحقيق الأرباح ضمن أولويات الشركة. كما أكدت نتائج التحليل الإحصائي وجود علاقة ذات دلالة إحصائية بين مخاطر إدارة سلسلة الإمداد والأداء التشغيلي،حيث أظهرت النتائج علاقة عكسية قوية، بما يدل على زيادة المخاطر تؤدي إلى انخفاض مستوى الأداء التشغيلي.
Maintenance Optimization in Cold Rolling Mills: A Case Study of LISCO
This paper presents a comprehensive study on maintenance optimization for the cold rolling mill stands at the Libyan Iron & Steel Company (LISCO). While predictive maintenance and simulation are well-established in global steel industry literature, a significant gap exists in applying these methodologies to the specific context of aging mills in North Africa, which face compounded challenges of legacy infrastructure, spare part shortages, and supply chain instability. This study aims to bridge this gap by developing a context-specific, data-driven framework. Building on a comprehensive analysis of historical production data, internal, external, and planned stoppage records, and key reliability metrics, the study identifies major causes of downtime and evaluates their impact on mill availability and productivity. Statistical reliability analysis and discrete-event simulation modeling are employed to support data-driven maintenance planning and resource allocation. The findings highlight critical areas for targeted maintenance interventions that can significantly reduce unscheduled stoppages and enhance overall equipment effectiveness. The primary contribution lies in the integrated application of Pareto analysis, OEE assessment, and simulation to formulate a prioritized action plan tailored to LISCO's operational realities.
Cold Rolling, Reliability, Simulation, Arena Software
Integrating Next Generation ERP Systems into Cement Factories:A Case Study Approach to Enhance Operational Efficiency and Sustainability
This study runs a full comparative analysis of manual and ERPNext procurement cycles in a cement manufacturing facility to identify efficiency gained from digital transformation. By using systematic real time analysis and workflow assessment. The research shows that ERPNext implementation lowers the procurement cycle times by 24-48% (from 9-19 days to 4.5-16.5 days) and also lowers human error rates by an approximation of 80%. The manual process faces major delays with a 40% of total cycle time consumed by internal administrative tasks compared to only 15% in the automated digital system. Although both systems sharing the same supplier response time as the same primary bottleneck of (50% of cycle time) ERPNext demonstrates a superior operational efficiency through workflow digital automation with realtime visibility and integrated data management that is a beginner user friendly. The calculated return on investment shows that an annual saving of LYD 260,000-640,000 for 100 procurement cycles annually which provides logical economic justification for ERP system adoption in a medium to large manufacturing enterprises
Enterprise Resource Planning, ERPNext, Procurement Automation, Digital Transformation, Process Efficiency, Manufacturing Operations, Supply Chain Management
BER Performance of 16-PSK in MIMO Systems under ACI and CCI over Fading Channels
With the growing demand for higher data rates and improved spectral efficiency in modern communication systems, understanding the ability of higher-order modulation schemes such as 16 Phase Shift Keying (16-PSK) to resist interference or fading conditions is essential, particularly under realistic channel impairments. This paper provides a performance analysis of 16-PSK modulation in a wireless system, under the impact of Adjacent Channel Interference (ACI) and Co-Channel Interference (CCI) across fading channel environments. Additionally, it's evaluating the system performance over both Rayleigh and Rician fading channels using various MIMO (Multiple-Input Multiple-Output) configurations, including 2×2, 2×3, and 2×4 setups. The simulation model was built using MATLAB Simulink to evaluate Bit Error Rate (BER) as a function of Signal-to-Interference Ratio (SIR) and Energy per Bit to Noise Power Spectral Density (Eb/N₀).
An Intelligent IoT and Machine Learning-Based Smart Irrigation System Using LoRaWAN
In this paper, we introduce the development of a smart agriculture system which unifies LoRaWAN-based Internet of Things (IoT) devices and machine learning in order to improve monitoring, cultivation, and watering processes in modernized agricultural activities. Based on Milesight IoT sensors and gateways, it monitors environmental data such as soil condition, air temperature, humidity, and wind speed 24/7 and transmits this information over a LoRaWAN network to a central cloud platform. The data is harvested by a Python backend and stored in an interactive Streamlit dashboard for real-time visualization and farm control. For efficient water usage, an irrigation prediction Random Forest Classifier was built based on a curated data set of 1,248 records and tested using unseen data. The model's accuracy approaches the perfect, with conductivity as the most important feature, correlated with the soil moisture. Cross-validation supported strong generalization with an average accuracy of 81.6%. The presented system highlights how integration of IoT and machine learning minimizes resource wastage, reduces manual interaction, and promotes sustainable robust agricultural practices. The implications of this study are to promote the development of -its-kind and scalable IoT-based smart farming systems in farms, so that farm resources can be more effectively utilized based on real-time dynamic data analytics
Performance Enhancement of 60GHz Microstrip Antennas Using Metamaterial-Based EBG and Lens
In this paper, metamaterial approaches are used to improve the gain, bandwidth, and radiation efficiency of microstrip patch antennas for 60GHz applications. In order to lower surface wave losses and improve the performance of proposed antennas, this work investigates a variety of techniques, including the use of metamaterials as antenna lens and as electromagnetic bandgap (EBG) structures. To overcome the narrow bandwidth (2 GHz) and low gain (1.9 dB) of a conventional GaAs-based microstrip patch antenna operating at 60GHz, several enhancements were investigated via HFSS simulations, the result were cross validated and compared using CST Microwave Studio. Integrating an Electromagnetic Band-Gap (EBG) structure improved performance to a 7.3GHz bandwidth and 3.5dB gain. The most substantial enhancement was realized by combining the EBG with a lens technique, achieving a 10GHz bandwidth and 4.8dB gain. These results confirm that metamaterial-inspired techniques are highly effective for advancing high-frequency antennas, particularly for 60GHz communication systems.
Performance Comparison of LQR Versus a Novel Hybrid Optimized PID Controller for Inverted Pendulum Stabilization
This study presents a comparative analysis of control strategies for the stabilization of an inverted pendulum system. It evaluates a traditional Linear-Quadratic Regulator (LQR) against PID controllers optimized using metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Furthermore, the investigation introduces and assesses novel hybrid controllers that combine the PSO and GA optimization techniques. The performance of all five control strategies was tested and compared through MATLAB/Simulink simulations, which included conditions with external disturbances to evaluate robustness. The results demonstrate that while all controllers successfully stabilized the system, the hybrid metaheuristic approach yielded a notably superior performance in terms of transient response. The study concludes that hybrid metaheuristic algorithms represent a highly promising method for controlling complex, nonlinear systems, particularly in applications where traditional control techniques are limited by their dynamic performance.
PID control, LQR, Particle Swarm Optimization, Genetic Algorithm, Hybrid metaheuristics, Inverted pendulum, Transient performance, Stability analysis
Comparative Analysis of PID Control Structures for Glucose Regulation in Type 1 Diabetes: A Simulation-Based Study Using the Bergman’s Model
Regulation of blood glucose levels is a critical challenge in the management of Type 1 Diabetes (T1D), where endogenous insulin production is impaired. This study presents a comprehensive comparative analysis of various classical control strategies for glucose regulation, based on the Bergman Minimal Model. We evaluate the performance of Proportional (P), Proportional-Integral (PI), Proportional-Derivative (PD), and Proportional-Integral-Derivative (PID) controllers, implemented in both series and parallel configurations.. The system is subjected to physiological disturbances, including meal intake, to simulate real-world conditions. Controller performance is rigorously assessed using a suite of metrics, including the Integral of Absolute Error (IAE), Integral of Squared Error (ISE), Integral of Time-weighted Absolute Error (ITAE), Integral of Time-weighted Squared Error (ITSE), and Integral of Control Effort (ICE), alongside time-domain specifications such as settling time, overshoot, rise time, and steady-state error. Our findings indicate that while the series PID controller demonstrates the most effective regulation, it demands a control effort that may be clinically impractical. In contrast, the parallel PID controller provides the best compromise between accuracy, stability, and insulin utilization, making it a more suitable candidate for automated glucose control systems. These results underscore the critical importance of controller structure in biomedical applications and highlight the potential of PID-based designs in the development of future artificial pancreas technologies The investigated system with applied different control strategy is implemented in MATLAB.
Type 1 Diabetes, Glucose Control, PID Controller, Bergman Minimal Model, Artificial Pancreas, Control Structures
Mechanical Properties of Concrete Utilizing Metakaolin - Cement Coated Recycled Aggregates
Research supports using recycled concrete aggregates (RCA) as an economical and eco-friendly substitute for natural aggregates (NA) in concrete. However, RCA's higher water absorption reduces its engineering performance compared to NA. To address this, pretreatment methods like thermal, mechanical, and chemical processes have been proposed. This study specifically tested the effect of metakaolin - cement coated recycled aggregates on the compressive and tensile strength of concrete where RCA replaced 10-20 mm natural coarse aggregate at levels of 0, 20, 30, 40, 50, and 100% (by weight). Results showed that while RCA addition generally reduces concrete strength, coating the aggregates mitigates this effect. Notably, concrete with 100% coated RCA showed compressive and tensile strength improvements of approximately 7.5% and 7.7%, respectively, compared to concrete with 100% untreated RCA.
Scalable Uplink Combining in Cell-Free Massive MIMO under Practical Fading and Centrized Processing
In this paper, we conducted a performance study of uplink combining algorithms for Cell-Free Massive MIMO, a future wireless technology aimed at removing cell boundaries to reduce inter-cell interference. In contrast to traditional Massive MIMO, Cell-Free deployments adopt multiple distributed access points (APs) that cooperate to serve users, thereby increasing spectral efficiency (SE) and service fairness. We analyze centralized combining schemes such as MMSE, MR, ZF, and RZF under both Rayleigh and Rician fading environments, considering fronthaul and complexity constraints. System-level simulations evaluate SE behavior for different numbers of APs and antennas. Comparisons indicate that the MMSE and RZF have achieved SE but with higher complexity. The 90%-likely SE is used as a measure of fairness and reliability.
Detection of Breast Cancer by a Circular Microstrip Patch Antenna (CMSA)
Microwave technologies are important and promising in breast cancer detection, which causes many deaths worldwide annually. These techniques have many ways of diagnosing and detecting breast cancer. In this paper, the Microstrip Patch Antenna (MSA) was presented as a source of microwave signals for use in breast cancer detection. A circular MSA was designed to operate at an operating frequency of 9 GHz, which falls within the ISM frequency range, and some improvements were added to the antenna to raise its efficiency. The antenna is designed in terms of a Rogers RT / Duroid 5880 substrate, with a height of 1.6 mm, to be used in simulating breast cancer detection. Simulation involves designing a breast model (Simulated breast) that simulates a human breast with specific dimensions based on natural breast tissue's electrical and physical properties, such as electrical permittivity and electrical conductivity. By studying antenna parameters such as return losses, directivity, gain, and current density, whose values differ with the presence of a tumor inside the breast, and comparing these values with the absence of a tumor inside the breast, the diagnosis and presence of cancer cells is reached. In this research, the CST STUDIO program was used to design both the antenna and the breast model. The CMSA antenna will be evaluated using a breast model containing a tumor with altered electrical properties. Electrical properties, particularly conductivity (σ) and permittivity (ε), are critical factors in determining the presence of a cancer within the breast.
Detecting Dust on Solar Panels using Deep Learning Models
Dust accumulation on solar panels is a well-known factor that reduces energy production and, if left unaddressed, can significantly affect system efficiency. Early and accurate detection of dust is therefore essential for effective maintenance and sustained power generation. This study presents an automated approach for identifying dust on solar panel using deep learning–based image classification. Two convolutional neural network (CNN) architectures, Xception and VGG16, were evaluated to assess their effectiveness in this task. To improve model robustness and generalization, an extensive preprocessing pipeline was applied to the dataset. The experimental results demonstrate that the Xception model achieved superior performance, reaching a classification accuracy of 98.82%, while the VGG16 model attained an accuracy of 93.49%. Beyond performance evaluation, this work emphasizes model interpretability. The integrated gradients method was employed to generate visual explanations of the models’ predictions, highlighting image regions that contributed most to the classification outcomes. These visual interpretations provide valuable insight into the decision-making process and enhance confidence in the results. Overall, the findings indicate that deep learning offers a reliable solution for automated solar panel monitoring, reducing reliance on manual inspection. Moreover, incorporating interpretability techniques improves transparency and supports practical deployment. This study contributes to renewable energy maintenance research and lays the groundwork for intelligent systems that integrate automated detection, cleaning strategies, and predictive maintenance to enhance the long-term efficiency of solar power installations
Damage Assessment of Reinforced Concrete Slabs Using Acoustic Emission and Numerical Modeling
This study presents an experimental and numerical investigation of damage development in reinforced concrete slabs subjected to monotonic loading. Acoustic emission (AE) monitoring was used to evaluate crack initiation and progression during loading. Time–frequency analysis techniques were applied to characterize changes in the frequency content of AE signals associated with damage evolution. The experimental results showed clear variations in acoustic emission characteristics as cracking developed and localized within the slab. To support the experimental observations, nonlinear finite element modeling based on the Concrete Damaged Plasticity model was performed. Good agreement was observed between the numerical tensile damage distribution and the acoustic emission activity recorded during testing. The results indicate that combining acoustic emission monitoring with numerical modeling provides a practical approach for assessing damage development in reinforced concrete structural elements.
Acoustic emission; Reinforced concrete slabs; Finite element modeling; Damage assessment; Crack development; Structural behavior
Machine Learning Approaches for Indoor Positioning: A Case Study on KNN vs WKNN-inv and WKNN-cos
Indoor localization is of paramount importance in intelligent environments, particularly in scenarios where Global Positioning System (GPS) signals are hindered by physical barriers within indoor settings. This study investigates machine learning methodologies designed to enhance the precision of indoor positioning systems. In particular, we conduct a comparative analysis of three algorithms: the conventional KNearest Neighbors (KNN), a Weighted KNN utilizing inverse distance metrics (WKNN-inv), and an additional variant that employs cosine similarity measures (WKNN-cos). Each algorithm was systematically implemented and assessed concerning its positioning accuracy. The results from simulations indicate that WKNN-inv yields an accuracy enhancement of approximately 11.2% relative to KNN, whereas WKNN-cos provides a 13.0% improvement.
محاكاة نظام تحكم ذكي باستخدام متحكم اردوينو لتنظيم عملية تعبئة شاحنات الوقود في مستودعات المصافي
يتناول هذا البحث مشكلة واقعية تتعلق بأتمتة عملية تعبئة شاحنات الوقود في المستودع الرئيسي للمصافي النفطية، حيث تُدار حالياً عمليات تعبئة أسطول متنوع من الشاحنات متعددة المقصورات بطريقة يدوية. ومن أجل تحسين كفاءة هذه العمليات وتقليل الخطأ البشري، تم تصميم وتطوير نظام آلي يعتمد على لوحة Arduino Uno R3 كمتحكم دقيق رئيسي. تم إنشاء نموذج تعبئة ذكي يهدف إلى توزيع أنواع الوقود المختلفة على مقصورات الشاحنات بكفاءة، كجزء من استراتيجية تحسين مسارات التوزيع نقل الوقود. صُمّم هذا النظام ليستخدم عددًا من الحساسات والمشغلات الصناعية لنقل واقع العمل، من بينها حساسات الموجات فوق الصوتية لتحديد موقع الشاحنة بدقة، ومستشعر بالأشعة تحت الحمراء لضمان محاذاة دقيقة لخرطوم التعبئة مع فتحة خزان الوقود، بالإضافة إلى وحدة قياس تتحكم في كمية الوقود المتدفقة. وتعرض البيانات التشغيلية بشكل لحظي على شاشة LCD لمراقبة العملية بدقة واستمرارية. ويهدف هذا النظام المقترح إلى خفض معدلات الخطأ البشري، ورفع مستوى السلامة والكفاءة التشغيلية، خاصةً في بيئة عمل تتسم بارتفاع مستويات الخطورة. أظهرت نتائج التجارب أن النظام يعمل بكفاءة وموثوقية عالية، وحقق أهدافه في تعزيز السلامة التشغيلية، تقليل الأعطال، وتوفير عملية تعبئة دقيقة وآمنة، مما يسهم في حماية المعدات وضمان سلامة العاملين والسائقين داخل منشآت المصافي
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محاكاة ودراسة تحسين أداء محطة جنوب طرابلس الغازية باستخدام التبريد بالهيدروجين
في هذه الدراسة تم تصميم نموذج لمنظومة محطة جنوب طرابلس باستخدام البرنامج العالمي أسبن بلس وبعد معايرتها ونتيجة لارتفاع درجة حرارة العادم مما يتطلب النظر في إمكانية استغلال الطاقة الحرارية المهدورة للعادم، ظهرت فكرة مبتكرة وهي إمكانية استغلال تلك الحرارة في انتاج الهيدروجين من خلال استخدام منظومة الإصلاح الخارجي وتكمن فكرة هذه التقنية بتفاعل الوقود (الديزل) مع بخار الماء لإنتاج الهيدروجين وثاني أكسيد الكربون ولكون ان هذا التفاعل ماص للحرارة تم تغطية احتياجات الطاقة من منظومة التوربين الغازي. وبعد إجراء عملية المحاكاة أظهرت النتائج عرض طريقة جديدة لاستقرار أداء المحطة الغازية على مدار العام وزد على هذه الميزة تم إنتاج الهيدروجين بمقدار (691.2 ton/day) من الديزل مما أدى إلى التحسين العام في أداء المحطة وهذا يتوقع ان ينعكس إيجابياً من الناحية الاقتصادية لكون أن الطاقة المهدورة للغازات تم استغلالها في انتاج الهيدروجين، مما يعني بصورة أخرى تقليل من الانبعاثات الكربونية والمحافظة على البيئة.
Hybrid Transform-Based MIMO-OFDM Framework for Audio Signal Transmission over Fading Channels
MIMO-OFDM, a combination of Multiple Input Multiple Output and Orthogonal Frequency Division Multiplexing, offers a powerful framework for high-quality audio transmission over wireless fading channels. This study investigates the performance of hybrid transform-based MIMO-OFDM systems using combinations such as FFT+DWT, FFT+DCT, and DCT+DWT, alongside modulation schemes including BPSK, QPSK, 8PSK, 16PSK, 16QAM, 64QAM, and 128QAM, across Rayleigh and Rician fading environments. Evaluation focuses on Bit Error Rate (BER) and Spectral Distortion over varying Signal-to-Noise Ratios (SNR). Results reveal that lower-order modulations (BPSK, QPSK) combined with DWT-based hybrids provide optimal BER and spectral performance, while higher-order QAM schemes suffer in fading conditions. The study highlights the importance of adaptive transform-modulation strategies to enhance audio fidelity and robustness in dynamic wireless scenarios.
Evaluating the Impact of Queuing Algorithms and Routing Protocols on QoS for VoIP Traffic over IP Networks
As internet usage and digital applications continue to surge, ensuring high-speed connectivity with dependable Quality of Service (QoS) for multimedia communications—particularly VOIP—has become increasingly vital. To meet this demand, various traffic management systems have been explored. This study conducts an in-depth analysis of widely adopted queuing methods (FIFO, Priority Queuing, and Weighted Fair Queuing) in conjunction with routing protocols (RIP, EIGRP, and OSPF), assessing their impact on QoS for VOIP traffic over IPv4 networks. Through comprehensive simulation experiments using Opnet 14.5A, the research evaluates each combination under varying traffic loads and network scales. Key QoS indicators such as jitter, packet loss, end-to-end delay, packet delay variation, and traffic throughput were measured. The objective is to determine which routing protocol most effectively complements each queuing strategy in optimizing QoS, and to identify the most robust pairings of routing and queuing approaches for enhanced VOIP performance in TCP/IP networks
Blockchain as a Solution for Internet of Things Security: a Survey
The Internet of Things (IoT) has revolutionized device interactions, enabling automation and connectivity, but security and trust issues have hindered its widespread adoption. Blockchain technology is seen as the optimal way to address these difficulties due to its immutable and decentralized characteristics. This article examines the capacity of blockchain technology to mitigate significant security and trust issues in the expanding IoT environment. This study conducts a thorough literature analysis to analyses how the decentralised, irreversible, and transparent characteristics of blockchain enhance security and trust in IoT networks. This study analyse and classify prevalent security concerns pertaining to the IoT layered architecture. Additionally, catalogue and illustrate the most common challenges in both blockchain networks and IoT network during the integration, and the advantages of this combination between those two technologies. Furthermore, the limitation of blockchain with some IoT application.
The Integration between IoT and blockchain.
Improving the Performance of Wireless Charging System for Electric Vehicles Using PS-SS Topology to Achieve High Power Transmission Efficiency
Highly efficient wireless power transfer system for charging electric vehicle batteries with high efficiency, based on a primary-series-secondary compensation (PS-SS) topology. The system is designed with a flat circular transmitter and receiver coil, a full-bridge inverter to convert DC to high-frequency AC, and a full-bridge rectifier on the receiving side to convert AC back to DC for battery charging. Resonant capacitors are used to adjust the frequency response and achieve resonant compatibility for the system . A working methodology based on accurate simulation using MATLAB was adopted to study the effect of several geometric variables on the efficiency of the system. The study included changes in the air gap, resonant capacitance, and load resistance . Four different operational cases were tested to evaluate the ultimate system efficiency and analyze its performance under realistic operating conditions. The results showed that optimizing, reducing the air gap , and precisely adjusting the capacitance values resulted in an efficiency exceeding 96% under optimal operating conditions. These results reinforce the importance of adopting the PS-SS topology with a well-thought-out engineering design to achieve reliable and highly efficient wireless charging systems suitable for modern electric vehicle applications
Abdulhafith Amhimmid Abuighrarah Abuighrarah
A Systematic Review of Artificial Intelligence Applications in Education through Metaverse and Holographic Communication Technologies
This paper presents a systematic review and conceptual framework on the applications of Artificial Intelligence (AI) in education through Metaverse environments and holographic communication technologies. Unlike experimental studies, the contribution is explicitly theoretical and strategic, synthesizing existing research to outline the potential of AI in enabling immersive and scalable educational systems. The review examines core techniques—neural rendering, predictive modeling, image enhancement, adaptive compression, and multisensory adaptation—highlighting their roles in supporting real-time holographic rendering, personalized learning, and interactive engagement. A multi-layered integration framework is proposed to link AI with volumetric streaming and neuroadaptive feedback systems. Representative use cases include collaborative classrooms, medical training simulations, and brain–computer interface (BCI)-based adaptive learning. Key technical and ethical challenges such as scalability, latency, privacy, and algorithmic transparency are identified. The study positions itself as a conceptual and strategic contribution, providing a focused foundation for advancing AI-powered holographic Metaverse ecosystems in education.
Integration of Solar Energy into the Libyan Power Grid: Challenges and Real-Time Optimization Approaches
This paper presents a comparative analysis of the challenges and solutions associated with integrating renewable energy sources into power grids, focusing on Libya as a representative post-conflict developing country. This research highlights the technical, economic, and policy related barriers hindering the adoption of solar and wind energy. By examining successful integration strategies employed in countries such as Germany, India, and others, the paper proposes actionable insights for Libya 's transition to a sustainable energy future. Key solutions discussed include smart grid development, energy storage systems, forecasting technologies, and cross-sectoral policy alignment
يتناول هذا البحث مشكلة واقعية تتعلق بأتمتة عملية تعبئة شاحنات الوقود في المستودع الرئيسي للمصافي النفطية، حيث تُدار حالياً عمليات تعبئة أسطول متنوع من الشاحنات متعددة المقصورات بطريقة يدوية. ومن أجل تحسين كفاءة هذه العمليات وتقليل الخطأ البشري، تم تصميم وتطوير نظام آلي يعتمد على لوحة Arduino Uno R3 كمتحكم دقيق رئيسي. تم إنشاء نموذج تعبئة ذكي يهدف إلى توزيع أنواع الوقود المختلفة على مقصورات الشاحنات بكفاءة، كجزء من استراتيجية تحسين مسارات التوزيع نقل الوقود. صُمّم هذا النظام ليستخدم عددًا من الحساسات والمشغلات الصناعية لنقل واقع العمل، من بينها حساسات الموجات فوق الصوتية لتحديد موقع الشاحنة بدقة، ومستشعر بالأشعة تحت الحمراء لضمان محاذاة دقيقة لخرطوم التعبئة مع فتحة خزان الوقود، بالإضافة إلى وحدة قياس تتحكم في كمية الوقود المتدفقة. وتعرض البيانات التشغيلية بشكل لحظي على شاشة LCD لمراقبة العملية بدقة واستمرارية. ويهدف هذا النظام المقترح إلى خفض معدلات الخطأ البشري، ورفع مستوى السلامة والكفاءة التشغيلية، خاصةً في بيئة عمل تتسم بارتفاع مستويات الخطورة. أظهرت نتائج التجارب أن النظام يعمل بكفاءة وموثوقية عالية، وحقق أهدافه في تعزيز السلامة التشغيلية، تقليل الأعطال، وتوفير عملية تعبئة دقيقة وآمنة، مما يسهم في حماية المعدات وضمان سلامة العاملين والسائقين داخل منشآت المصافي.
Effective Control of a Robotic Manipulator's Trajectory Tracking through the utilization of an Optimal Linear Quadratic Gaussian (LQG) controller enhanced by the Improved Particle Swarm Optimization (IPSO) Algorithm
This paper introduces a technique for modeling and effectively controlling the trajectory tracking of a robotic manipulator through an LQG controller optimized by the IPSO algorithm. Extensive simulations have been conducted within the MATLAB simulation environment. The LQG, which combines a Kalman Filter (KF) and a Linear Quadratic Regulator (LQR), is developed to follow the desired input for the robotic manipulator while minimizing the impact of process and measurement noise on its performance. The parameters of the LQG controller, which include the elements of the state and control weighting matrices for the LQR and KF, are optimally adjusted using the IPSO method. The validation of the proposed hybrid LQG-IPSO controller is conducted based on the control criteria parameters. The findings demonstrate that the proposed hybrid LQG-IPSO controller can achieve excellent movement performance. To fully leverage the capabilities of the IPSO algorithm, careful adjustment and determination of IPSO parameters such as inertia weight, iteration count, acceleration constants, and particle quantity are essential. Therefore, The initial focus of this study is to conduct a comparative analysis of various fitness functions; simulation results indicate that the proposed hybrid LQG-IPSO control method achieves commendable fitness outcomes with minimal steady-state error (ess). Following this, a comparison of the performances of a robotic manipulator using the hybrid LQG-IPSO controller, hybrid LQR-IPSO controller, LQG controller, and LQR controller is also presented. Based on the analysis, it can be concluded that superior performance parameters are attained with the hybrid LQG-IPSO controller when compared to its counterparts—the hybrid LQR-IPSO controller, LQG controller, and LQR controller—offering enhanced performance.