The Journal of Technology Research (JTR) is a peer-reviewed, open-access scientific journal, published in both print and electronic formats by the College of Industrial Technology – Misurata. The journal covers all fields of engineering and technology, serving as a scientific platform for the dissemination of knowledge. Since all submissions undergo rigorous review by highly qualified specialists, the published research is distinguished by the novelty of its information, the diversity of its topics, and the accuracy, integrity, and objectivity of its contributions. This makes the journal a valuable reference for researchers and an essential resource for researchers across different academic levels. This guide outlines the policies and internal regulations of the Journal of Technology Research. Vision: Leadership and excellence in publishing technical and industrial research locally, regionally, and internationally, serving the community and advancing the scientific research environment. Mission: The Journal of Technical Research is committed to publishing original, peer-reviewed scientific research across various technical, engineering, and industrial disciplines, while promoting creativity and innovation according to the highest standards of scientific publishing. Objectives: 1. Publish original, peer-reviewed research in technical, engineering, and industrial fields. 2. Enhance the position of College of Industrial Technology – Misurata. as a distinguished center for applied scientific research. 3. Provide a scientific platform for researchers to share their ideas and contributions in support of sustainable development. 4. Continuously improve the quality of research and publication services in line with the latest scientific and technological advancements. 5. Raise the level of scientific publishing in accordance with local and international standards.
College of Industrial Technology - Misurata
Mapping Olive and Palm Trees in Libya Using Computer Vision: A Baseline Study for Food Security and Economic Diversification
The cultivation of olive and date palms is an integral part of the Libyan agricultural sector and contributes significantly to food security and economic diversification. However, the absence of accurate and up-to-date national data on tree populations limits strategic planning and investment. This study introduces an integrated approach using high-resolution satellite imagery, UAV surveys, and AI-based tree detection models to map olive and palm trees in Libya. The project aims to establish the first national baseline for tree populations, providing insight into spatial patterns, productivity potential, and vulnerability hot spots. The study offers actionable recommendations to strengthen sustainable agriculture and economic diversification efforts by linking tree distribution data to food security and economic indicators. The methodology also highlights the transformative potential of information and communication technologies (ICT) and remote sensing in shaping agricultural strategies within fragile contexts like Libya.
Copyright (c) 2026 Journal of Technology Research https://creativecommons.org/licenses/by-nc/4.0
Improving Rebar Production Processes Through Six Sigma Methodology
Improving productivity and reducing waste in manufacturing processes is a primary objective for all industrial organizations. Theoretical Six Sigma calculations were applied to actual data collected from the heat-treated and mechanically treated (TMT) steel bar plant of the Libyan Iron and Steel Company (LISCO). This was achieved by applying the Six Sigma (Definition, Measurement, Analysis, Improvement, and Control) methodology and using tools such as control charts, Pareto analysis, cause-and-effect diagrams, and SPSS software, with the aim of optimizing the manufacturing process of heat-treated and mechanically treated steel bars. The results obtained from the applied calculations showed a promising level of improvement, with the production waste rate decreasing to approximately 1.7%. The process parameters affecting the mechanical properties of heat-treated and mechanically treated steel bars were also identified. Therefore, it can be concluded that the study has yielded valuable and encouraging results.
Comprehensive review on Water Energy Dissipation by using Hydraulic Jump
This research paper looks at how energy dissipation is important for hydraulic structures, especially spillways, to reduce local scour and prevent structural failures. It examines traditional and new techniques for energy dissipation by formation of hydraulic jump, focusing on the most used appurtenances in stilling basins. Comprehensive review addresses how gravitational potential energy turns into kinetic energy, which affects shear stress and sediment transport. It details different methodologies for energy dissipation, the role of the Froude number, standardized designs from the U. S. Bureau of Reclamation, Type I, II, III and IV and enhancements such as baffle blocks, end sill and baffled chutes. Experimental results validate these designs to improve hydraulic performance and manage costs effectively.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Evaluating The Impact Of Time-To-Exploit Estimation For Vulnerability Prioritization
Recently, security vulnerabilities have increased significantly, as this study addresses the issue of prioritizing them by developing a predictive model that estimates the time required to exploit them. Data obtained from multiple sources was used to develop this model, including a unified Kaggle dataset, which combines data from three reliable sources: the National Vulnerability Database (NVD), the CISA Known Exploitable Vulnerabilities (KEV) list, and the Exploitation Prediction Score System (EPSS). Data from both ExploitDB and CISA KEV list was also used. The dataset was divided into training (2021-2023) and testing (2024) sets, to compensate for the lack of confirmed exploitation dates, isotonic regression was used to model the monotonic relationship between EPSS scores and actual exploitation dates, as a methodological alternative. We also evaluated three regression models: the best results for the test set were shown in the XGBoost model (MAE=2.98 days, RMSE=12.20 days, R²=0.936, MAPE=14.43%), while the Random Forest performed the baseline linear regression model (MAE=2.77, RMSE=14.59, R²=0.908, MAPE=13.43% vs. MAE=18.48, RMSE=24.57, R²=0.740, MAPE=51.50%). To interpret these predictions into actionable information, the estimated "Time To Exploit" was transformed into a "Composite Priority Index" that combines the predicted speed of exploitation with the probability score, the Exploitation Potential Scoring System (EPSS) was then used to categorize vulnerabilities into the following levels: urgent, high, medium, and low. This approach improved our ability to identify high-risk vulnerabilities early by incorporating time-based data, compared to relying solely on static criteria.The results show that incorporating the time dimension enhances its reliability and wider applicability
Exploit Prediction Scoring System (EPSS)
Hybrid Deep Learning and Information Flow-Based Fuzzy Cognitive Maps for Explainable Predictive Maintenance in Collaborative Robotics
Predictive maintenance (PdM) in collaborative robotics (cobots) faces a critical dilemma: while deep learning models offer high accuracy, they lack interpretability, and rule-based systems are transparent but insufficiently adaptive posing a serious challenge in safety-critical Industry 5.0 environments where both performance and explainability are non-negotiable. To resolve this trade-off, this paper proposes a novel hybrid architecture that synergistically combines a Convolutional Recurrent Neural Network (CRNN) for high-fidelity fault prediction with an Information Flow-based Fuzzy Cognitive Map (IF-FCM) for human-interpretable causal reasoning. Unlike prior approaches that rely on heuristic or static FCM weights, this research IF-FCM is automatically calibrated using the CRNN’s latent representations and data-driven causal discovery: edge weights are derived from transfer entropy (for directional influence) and mutual information (for co-variability), eliminating expert bias and enabling dynamic, physics-grounded explanations. Evaluated on the real-world UR3 CobotOps dataset from the UCI repository, the model achieves state-of-the-art performance with 97.8% accuracy, a 0.983 F1-score, and a 0.991 AUC while generating expert-validated explanations with 89% consistency (inter-rater κ = 0.81). A key advantage is a 34% reduction in false alarms through context-aware reasoning (e.g., ignoring isolated thermal spikes without corroborating electrical anomalies). Furthermore, domain-constrained min-max normalization, aligned with manufacturer-specified physical thresholds, ensures semantic fidelity and model stability. The framework outperforms leading baselines, including CNN-LSTM, Attention LSTM, XGBoost+SHAP, and static FCMs across all metrics. This work’s primary contributions are (1) a closed-loop hybrid architecture that unifies deep learning and causal interpretability; (2) the first integration of information-theoretic measures into FCM learning for robotic PdM; and (3) a trustworthy, scalable solution that meets regulatory and operational demands for transparent AI in human-robot collaboration.
Collaborative Robotics, Fuzzy Cognitive Maps, Deep Learning, Transfer Entropy, Industrial Cyber-Physical Systems
Building an AI-driven assistant for the Administrative and Legal Domain
The rapid growth of digital information has created significant challenges in managing, organizing, and retrieving knowledge, particularly within legal and administrative domains. Conventional keyword-based search tools often fail to capture the contextual meaning of complex documents, leading to inefficiencies in decision-making and legal research. To address this issue, this study presents the design and implementation of an AI-driven assistant that integrates vector databases with large language models (LLMs) to support administrators and legal professionals. The proposed system leverages semantic embeddings to transform unstructured legal and regulatory texts into high-dimensional vector representations. Experimental evaluation was conducted using a legal dataset from the Libyan Academy for Telecom and Informatics (LATI), comprising more than 200,000 words of official laws and regulations. The system was tested with legal queries and assessed both automatically and through expert review. Results demonstrated that the assistant retrieved accurate, contextually relevant passages, significantly reducing response times compared to manual search. Legal experts confirmed that most answers were precise and practically useful, although further refinement is required for handling ambiguous or nuanced cases. The system improves administrative and legal efficiency by enabling semantic search beyond keyword matching and providing actionable insights for decision-makers. Future work will focus on expanding the legal corpus, refining query handling, and exploring advanced indexing techniques to improve scalability, accuracy, and adaptability across different domains.
Large Language Model, Vector Database, AI-driven Assistant
Arabic Fake News Detection Using Genetically Optimized Deep Learning Models
In recent years, the proliferation of fake news across social media and digital platforms has significantly undermined public trust in traditional media outlets, raising serious challenges at the social, political, and health levels. Within the Arabic context, this problem is further exacerbated by the scarcity of effective tools for Arabic language processing. This study proposes a hybrid model that combines AraBERT for extracting deep contextual representations, Convolutional Neural Networks (CNN) for capturing local features, and a Genetic Algorithm (GA) for hyperparameter optimization. The model was evaluated on an Arabic dataset consisting of real and fake news articles, while class imbalance was addressed using class weights. Experimental results show that the GA-optimized AraBERT-CNN hybrid model outperformed both traditional (LSTM) and modern (pure AraBERT) baselines, achieving an overall accuracy more than 96% and higher F1-scores for the minority class (real news). The significance of this study lies in presenting a comprehensive framework that integrates the contextual power of transformer-based models with the adaptive optimization of evolutionary algorithms, thereby contributing to ongoing efforts to combat misinformation in Arabic and to develop more reliable intelligent news verification systems.
Arab fake news detection, BERT, CNN, deep learning, genetic algorithm
Cross-Platform Performance Prediction of Software Applications Using Machine Learning Models for Optimized Resource Utilization
Efficient cross-platform performance prediction and resource optimization are critical for deploying software applications in heterogeneous computing environments. Existing models often struggle to capture complex software–hardware dependencies and evolving workload dynamics, resulting in limited prediction accuracy and poor adaptability. To overcome these limitations, this study introduces a unified intelligent framework that seamlessly integrates Platform-Aware Graph Attention (PAGA), CrossPlatform Temporal Memory (CPTM), and NSGA-II optimization. This tri-layer integration uniquely combines structural dependency learning, temporal sequence understanding, and multi-objective optimization to deliver adaptive and generalizable performance prediction across diverse hardware platforms. The model is implemented in Python using PyTorch for deep learning components and NumPy/Matplotlib for analysis. Experiments are conducted on a cloud performance dataset (CPU, memory, network, energy, execution time, instruction count) from Kaggle. The proposed framework achieves high prediction accuracy with MAE = 0.087, RMSE = 0.132, and R² = 0.97, marking a 20– 25% improvement over baseline models. In the optimization stage, NSGA-II achieves 26.8% execution time reduction, 23.5% energy saving, and 17.4% memory utilization improvement. These results highlight the novelty and effectiveness of the integrated PAGA–CPTM–NSGA-II architecture, demonstrating its potential for scalable, resource-efficient, and cross-platform software performance management in real-world deployments
Cross-Platform Performance Prediction, Energy-Efficient Computing, Meta-Learning, Resource Utilization Optimization, Software Performance Modelling.
Predicting Graduation GPA and Study Duration using Machine Learning: A Case Study of College of Industrial Technology Misrata, Libya
The current study aims to use different Machine Learning models to predict the graduation Grade Point Average (GPA) and the duration of graduation in terms of semesters, based on a student’s demographic and academic data from the College of Industrial Technology located in Misrata, Libya. The model takes into consideration the demographic data of students; student's gender and the age at which they joined the college. The GPA and the type of the secondary school and year of graduation, as well as the scores in four basic subjects are also considered as academic data. The purpose of the study is to enhance the academic management processes through more informed decisions and the provision of timely aid to students likely to fall behind, underperform, or show significant delay in achieving their academic milestones. The research analyses the performance of various algorithms, and offers suggestions for the advancement of support systems intended for academic counsellors in the institution.
Hydrochemical study of shallow groundwater in some localities in Zliten city
This study was conducted to identify some properties of underground water for located in the Souq Atholatha area, southeast the center of Zliten city to evaluate the wells, their suitability for drinking. Nine samples were collected from three wells, with depths ranging from 40-300meters.The investigated properties of the underground water are the pH, the total dissolved solids (TDS) and the electrical conductivity (E.C), nitrates (No3), manganese (Mn), sodium (Na), potassium (K), total hardness (TH), calcium (Ca), and magnesium (Mg). The results were recorded for maximum concentration in the wells: 8.13, 5801 mg/L, 7332 µcm/s, 19.8 mg/L, 0.12 mg/L, 1334 mg/L, 74.1 mg/L, 1532 mg/L, 345.85 mg/L, and 167.5 mg/L respectively. The results showed that all wells are not suitable for drinking. because water samples not satisfy the Libyan specifications.
A Systematic Approach to Selecting the Proper WLAN Solution
A Wireless Local Area Network (WLAN) utilizes radio frequency technology to enable communication within limited environments such as homes, enterprises, universities, and public spaces. Unlike wired networks, WLANs provide greater mobility, lower deployment costs, and enhanced flexibility and scalability. However, selecting the most proper WLAN solution remains a big challenge due to the varied and progressing users’ requirements. By categorizing WLAN technologies based on their standards, architectural designs, and security features, this work offers a methodical framework to aid in the WLAN selection decision-making process. It also pinpoints important client needs and creates an organized mapping between these needs and matching WLAN setups. The study presents an improved logical model that assists users and businesses in choosing the most suitable WLAN solution, guaranteeing coherence between technical capabilities and organizational requirements.
Behavior-Driven Semantic Re-Ranking for Personalized Web Search
As the web continues to expand, users face growing challenges in locating relevant and personalized information. Traditional search engines, while powerful, often deliver generic results that fail to account for individual user preferences, leading to inefficient search experiences. This study proposes a personalized re-ranking system that enhances standard search engine results by incorporating user behavior and semantic analysis. Leveraging BERT embeddings and KeyBERT for user profiling, the system dynamically reorders Google Search results to better align with users' historical interests and current queries. A user study involving 14 participants demonstrated that the proposed system significantly improves search efficiency, reduces effort, and enhances result relevance. These findings contribute to the development of adaptive search interfaces that personalize web search in real time without altering the core search infrastructure.
An Experimental Study to Investigate the Effect of Concrete Crack Dimensions on the Effectiveness of SIKADUR 31-CF SLOW in Concrete Repair Works
This study aimed to evaluate the effect of concrete crack dimensions (width and depth) on the repair efficiency of Sikadur 31-CF Slow by comparing flexural strength before and after repair and calculating mechanical recovery percentages. The results confirmed that variations in crack dimensions play a pivotal role in determining the material's effectiveness. The uncracked reference samples exhibited a flexural strength of 3.85 MPa, which was adopted as the standard value for comparison. When repairing cracks with a narrow width (1 mm) and a moderate depth (5 cm), the recovery rate reached 95%, while it decreased slightly to 92.63% as the depth increased to 7 cm, reflecting the effect of depth on reducing efficiency despite a constant width. The widest (5 mm) and deepest (7 cm) cracks recorded the lowest repair efficiency at 35.67%, indicating that increasing both width and depth simultaneously doubles the loss of mechanical properties after repair. It was also found that small, shallow cracks (1 mm x 2 cm) in the tensile strength test achieved recovery rates exceeding the reference (104.37%), which may be attributed to improved cohesion of the repaired layer and more homogeneous stress distribution. Conversely, increasing crack dimensions led to a significant decrease in recovery rates, in some cases falling below 60% of the original strength. These results confirm that controlling crack dimensions is a key factor in ensuring the optimal performance of Sikadur 31-CF Slow. Controlling natural cracks means treating them as soon as they occur, before they worsen, to limit their development and maintain the structural integrity of the concrete element. Treatment materials generally demonstrate greater efficiency in repairing small cracks compared to large ones. Therefore, optimal repair strategies should aim to reduce crack width and depth to the limits that allow for the highest levels of mechanical strength recovery in the concrete.
Concrete repair, Sikadur31-CF Slow, crack dimensions, crack location, load effect, mechanical properties, epoxy.
Copyright (c) 2025 Journal of Technology Research https://creativecommons.org/licenses/by-nc/4.0
Heat Exchanger Network Retrofit for Energy Savings in a Catalytic Reforming Naphtha Unit at Zawia Oil Refinery Using Pinch Technology
Energy conservation in oil refineries is essential for reducing operational costs and minimizing environmental emissions. One effective approach is optimizing the Heat Exchanger Network (HEN) design. In this research, pinch technology was applied to optimize the preheat exchanger network of the catalytic reforming unit at the Zawia Oil Refining Plant. The optimized configuration simplified the network to one heater and three coolers, significantly reducing the need for external utilities. The analysis was performed using HINT software. Results showed that the optimal minimum temperature difference (ΔTmin) is 25°C, while the pinch point was identified at 212.5°C. The minimum heat required from hot utilities was 4450.09 kW, and 3932.73 kW needed to be removed by cold utilities. The annual operating cost for hot and cold utilities was estimated at $573,329.7, while the annual capital cost was $100,108.7, resulting in a total annual cost of $673,438.4. Although these values do not represent the total plant energy savings, they highlight the strong potential of pinch analysis to improve thermal efficiency, reduce energy waste, and support sustainable operations in energy-intensive industries.
Identification of the Optimal Electrical Interconnection Point for Exporting Solar Energy to the Southern European Countries
With a growing demand for electric power, interconnecting national grids is crucial for ensuring stability and creating economic benefits through energy export1. This paper evaluates the transmission lines of the Libyan Electrical Network to determine their capacity to accommodate additional power for export the electrical energy to the Southern Europe Countries. The study simulates the injection of power from new photovoltaic (PV) stations in Ghadames, Asdaada, Hoon, and Bani Walid, and then analyses the grid's stability when this power is withdrawn from two potential coastal interconnection points, Al-Garaboly and Al-Khoms. Using NEPLAN software, network performance was assessed under normal (N) and contingency (N-1) conditions across three scenarios, which are: the existing Libyan grid scenario, and changing the existing ratio of Current Transformer (CT) scenario, the final scenario is integration of new 400 kV lines currently under construction. The results indicate that Al-Khoms is a more robust interconnection land point than Al-Garabuli. The most effective strategy for increasing export capacity involves activating the new 400 kV lines, which allowed for a maximum stable export of 1430 MW from Al-Khoms Station and 660 MW from Al- Garaboly Station under N-1 conditions. The evaluation of the transmission network is a fundamental aspect in selecting the optimal connection land point. However, several other considerations influence the final decision regarding the connection, such as the route of the submarine cable. These considerations include environmental studies that account for sensitive areas such as coral reefs and ecological impacts, in addition to oil concessions and military sites etc. It is essential to consider these factors to determine the best electrical connection point.
HVDC, Grid Interconnection, Libyan Power Grid, Power System Analysis.
Applied Analysis and Classification of the Physical and Chemical Properties of Selected Libyan Crude Oils
Libya’s current recovery plans hinge on maximising value from existing crude streams without the luxury of massive capital expenditure. In this context, a precise understanding of how Libyan crudes differ in their physical and chemical personalities is no longer academic—it is an operational necessity. We present an integrated laboratory campaign that captures three geographically distinct crude arteries (Brega, Nafoora, and Bouri) and subjects them to the most current ASTM/IP protocols for density, viscosity, pour point, flash point, elemental (C/H/S) analysis, metals (Ni, V), and asphaltene quantification. .Brega emerges as an exceptionally light (42.4 ° API), sweet (0.9 wt % S) and low-metal crude. Its quality makes it an excellent candidate for simple refining configurations like topping units, or as a premium feed for FCC gasoline production after distillation. Bouri sits at the opposite corner of the matrix: heavy (25.8 °API), sour (1.86 wt% S), and asphaltene-rich (21.3 wt%). Nafoora occupies the middle ground, inviting a selective hydro-treating route. Beyond the numbers, we reconcile the data with the underlying geology and offer pragmatic guidelines for blending, desalter design, pipeline hydraulics, and refinery revamps. The dataset is provided in full as electronic supplementary material to enable independent verification and future modelling work.
Libyan crude oil, physical properties, chemical properties, API gravity, sulfur content, classification, refinery configuration.
Optimising Production Portfolios for Profitability in a Libyan Bottled-Water SME: A Linear Programming Case Study of Shimaa Food Industries Company
In the highly-competitive Libyan bottled-water market, Shimaa Food Industries Company faces the recurrent dilemma of allocating limited resources among four product sizes (200 mL, 330 mL, 500 mL and 1.5 L) while maximizing monthly profit. This study develops and validates a deterministic Linear Programming (LP) model that integrates real production costs, market demand limits, and technological capacity constraints. Using verified 2023 operational data, the model was solved with the Simplex algorithm via Python-SciPy and verified with Excel-Solver. The optimal production plan recommends 14,250, 25,000, 20,000, and 15,000 cartons per month for the four sizes respectively, yielding a maximum attainable profit of 150,975 LYD, an increase of 18.7% over the current heuristic plan. Sensitivity analyses were expanded to confirm solution stability within ±10 % price fluctuations and ±5 % sales capacity deviations, and to specifically examine the impact of raw material cost volatility. The paper contributes an evidence-based decision-support tool that can be embedded in Shimaa’s Sales & Operations Planning (S&OP) cycle and offers a replicable framework for similar Small and Medium Enterprises (SMEs) in the MENA region.
Linear Programming, Profit Maximization, Bottled Water, Production Planning, Case Study, Libya
Hybrid Stacking Ensemble Model for Phishing URL Detection Using PCA and Machine Learning
The rapid growth of internet usage has transformed cybercrime into a formidable global challenge. Among digital threats, phishing stands out as particularly dangerous due to its deceptive approach. Cybercriminals employ fake yet convincing URLs to steal users' sensitive information, causing significant financial and personal damage. This escalating threat demands advanced countermeasures. This study addresses this critical need by proposing a hybrid machine learning (ML) model specifically designed to improve malicious URL identification. The innovative model integrates three powerful algorithms: Logistic Regression (LR), Artificial Neural Networks (ANN), and Random Forest (RF). These are combined within an advanced stacking ensemble architecture that strategically leverages each algorithm's unique analytical capabilities. This multi-layered approach enables comprehensive threat analysis from different perspectives. To optimize model efficiency and performance, we implemented Principal Component Analysis (PCA) for intelligent feature selection, ensuring optimal computational resource utilization. Our research utilized a substantial dataset of over 11,000 carefully labelled URLs sourced from Kaggle. The dataset underwent meticulous preparation, including appropriate balancing techniques to mitigate class imbalance issues that could compromise model accuracy. Through rigorous evaluation using key performance metrics accuracy, precision, recall, and F1-score the model demonstrated exceptional efficacy. Remarkably, the hybrid ensemble achieved an outstanding accuracy of approximately 99.55%, significantly surpassing all individual base models. This superior performance highlights the model's strong potential for immediate deployment in real-time phishing detection systems. It offers organizations a proactive and reliable defence mechanism in the ongoing battle against evolving cyber threats, representing a significant advancement in cybersecurity protection for today's sophisticated digital landscape.
Effect of Graphite Content on the Physical and Mechanical Properties of Self-Lubricating Iron-Based Composites Fabricated by Powder Metallurgy
This study investigated the physical and mechanical properties of a self-lubricating iron-based material reinforced with different weight percentages of graphite. The specimens were prepared using the powder metallurgy technique by designing and fabricating a suitable die for compacting sponge iron powders containing graphite in weight ratios ranging from 0% to 20%, followed by sintering at 1200 °C. The prepared samples were subjected to several laboratory tests, including green strength, green and sintered density, porosity, dimensional changes, hardness, compressive strength, and abrasive wear, to evaluate the effect of graphite content on the physical and mechanical properties. Microscopic examination was also carried out to track microstructural changes and relate them to the obtained results. The findings showed a decrease in porosity and an increase in density after sintering, accompanied by a 24% improvement in hardness. In the abrasive wear test, the addition of graphite reduced the average weight loss by about 74%, with the 1 wt% graphite sample exhibiting the best performance. All graphite-containing samples demonstrated improved wear resistance exceeding 340%, reaching a peak of about 570% for the 1 wt% graphite specimen compared with the pure iron sample. However, samples with higher graphite contents (≥ 5 wt%) showed separation of graphite from the iron matrix during sintering. Microscopic analysis confirmed that from 2.5 wt% graphite onwards, the graphite phase increased in size and formed voids within the structure, leading to deterioration of mechanical properties. The study concluded that incorporating 1 wt% graphite provides the optimum composition, offering an effective balance between self-lubrication and mechanical strength, and is therefore suitable for use in self-lubricating bearings and power transmission components.
Abrasive wear; Composites; Graphite; Powder metallurgy; Self-lubricating materials; Sponge iron.
Solving linear Programming Problems Using The Grey Wolf Optimizer in R
This paper presents the Grey Wolf Optimizer (GWO), a metaheuristic algorithm inspired by the leadership hierarchy and cooperative hunting strategies of grey wolves, as an approach for solving linear programming models. The algorithm was implemented in R language using advanced programming techniques and the functions available in the MetaheuristicOPT library. Several linear programming problems were tested, and the proposed algorithm successfully obtained optimal solutions. The performance of GWO was compared with the classical Simplex method, Genetic Algorithms, and TORA Software. Results indicate that GWO is efficient and can be used effectively for solving such problems in operations research applications.
Enhancing Solar Power Generation in Libya Using Solar Tracking Systems: A Study to Determine the Optimal Technology
Libya enjoys an average of up to 3,200 hours of sunshine annually. However, when using fixed solar panels, only around two hours of vertical sunlight can be effectively utilised due to the sun’s rotation around its axis and the Earth. Since solar panels generate electricity most efficiently when the sunlight falls perpendicularly on their surface, there is a need to enhance these systems in order to increase the number of effective hours throughout the day. For this purpose, single-axis solar tracking structures are selected. These systems are equipped with tracking devices that follow the sun from early morning until sunset, thereby increasing the productivity of power generation. Another type of solar tracking system is the dual-axis structure, but this is neither necessary nor suitable in Libya due to the stability of the tilt angle. Dual-axis systems are more costly and are generally used in countries where the solar angle varies significantly. In Libya, the tilt angle remains largely stable, with only minor variations from east to west, which can be adjusted based on the site of the solar farm and kept fixed while still tracking the sun’s movement from sunrise to sunset. Studies have shown that using single-axis solar tracking structures can achieve up to 35% higher productivity compared to fixed solar panel structures, with lower cost and maintenance requirements than dual-axis systems.
Energy Yield, Tilt Angle, Renewable Energy, Solar Panel Mounting Structure.
Economic Feasibility Analysis of Grid-Connected Solar Power Plants in Desert Regions
The electricity crisis in Libya is a complex crisis, combining technical and administrative components. One of the most prominent solutions to address the problem of increasing demand for electricity is the use of solar energy. Desert areas are among the best locations for establishing solar power plants due to the abundance of solar radiation, the vast unused areas, and the low cost of land. Grid-connected solar power plants represent a cost-effective investment in most regions, especially as costs continue to decline and efficiency increases. The success of these projects depends on careful planning and a detailed feasibility study for each individual site. This study evaluates the generating capacity of solar photovoltaic systems at multiple locations in desert areas within Libya. In this research, to model the physical behavior of the power system, in addition to estimating the total cost of installing and operating the system during its expected lifetime, the Homer program was used. Through the results extracted from the economic feasibility analysis of the hybrid solar energy system (330 kW panels and 280 kW inverter) connected to the public grid, the data shows that the project has a high economic feasibility. Given that the cost of generating electricity was extremely low (0.02425 kWh), the total project cost was approximately 316,295 Libyan dinars. The data also confirms that solar panel costs represent the largest portion of the project's overall expenditures. Finally, solar power plants connected to the public electricity grid represent a strategic option with high benefits in the medium and long term.
Solar Energy, Electrical Gird, HOMER, Economic Analysis of Renewable Energy.
Accurate Segmentation of Imbalanced Medical Images: A Comparative Study
Medical image segmentation is a fundamental task for accurate automated diagnosis, treatment planning, and clinical decision-making. This study presents a comparative evaluation of the LeViT-UNet model a convolutional encoder decoder network enhanced with transformer blocks on imbalanced computed tomography (CT) datasets. Two loss functions were investigated: the traditional Focal Loss and the composite Focal-Tversky Loss. The model was trained and validated on annotated CT slices exhibiting high class imbalance to assess segmentation accuracy and convergence stability. Experimental results reveal that training with Focal Loss enables faster convergence and achieves higher Dice and Jaccard scores during early epochs by emphasizing challenging samples. In contrast, the Focal-Tversky Loss achieves a better trade-off between sensitivity and specificity, leading to improved stability and generalization across imbalanced data. These findings underscore the importance of selecting task-specific loss functions for medical image segmentation and demonstrate that integrating LeViT-UNet with Focal-Tversky Loss provides a robust and consistent framework suitable for clinical applications demanding high precision.
Random Forest-Driven Feature Importance Assessment for QoS in MPLS and SD-WAN
This study presents a novel comparative analysis employing Random Forest regression to quantify the relative importance of key Quality of Service (QoS) parameters—packet loss, delay, and jitter—in Multiprotocol Label Switching (MPLS) and Software-Defined Wide Area Network (SD-WAN) architectures. Using empirical data collected from controlled simulations of multimedia traffic, the feature importance scores reveal that packet loss overwhelmingly dominates as the critical factor influencing network performance, with scores of 0.8620 in SD-WAN and 0.7259 in MPLS, indicating an 18.76% increase in SD-WAN’s sensitivity to packet loss. Delay exhibits moderate relevance in MPLS, with an importance score of 0.2205, but shows markedly reduced significance in SD-WAN at 0.1341 (a 39.21% decrease). At the same time, jitter demonstrates negligible influence across both networks, with scores below 0.054. These findings confirm that SD-WAN’s dynamic path optimisation effectively mitigates delay effects, whereas packet loss remains the principal constraint on performance. This work constitutes the first methodical Random Forest-based comparative evaluation of QoS parameter importance across MPLS and SD-WAN, delivering robust, data-driven insights tailored to each architecture’s operational characteristics. The framework provides network operators with critical guidance for targeted QoS optimisation, prioritising packet loss mitigation strategies, particularly within SD-WAN environments. Overall, this research establishes an empirical foundation for architecture-specific QoS management, advancing intelligent network performance assessment through machine learning techniques.
Supervised Machine Learning Approaches for Robust DDoS Detection in Cloud Environments
In today's landscape, the widespread adoption of cloud computing has been accompanied by a corresponding increase in security vulnerabilities, with Distributed Denial-of-Service (DDoS) attacks posing one of the most serious challenges by overwhelming resources such as CPU power, memory, and network bandwidth, thereby disrupting services for legitimate users. Detecting DDoS attacks in cloud environments is particularly difficult due to the similarity between malicious and legitimate traffic, often originating from numerous geographically dispersed sources. This study evaluates the effectiveness of five supervised machine learning algorithms Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Naïve Bayes (NB) for detecting DDoS attacks in cloud computing environments using the publicly available Software Defined Networking (SDN) DDoS Attack Dataset. Comprehensive preprocessing including normalization, feature selection, and Synthetic Minority Oversampling Technique (SMOTE) was applied, along with rigorous regularization strategies to mitigate overfitting. Experimental results demonstrate that Random Forest achieved the highest balanced performance (95% accuracy, 96% precision, 95% recall), followed by KNN (94%), SVM (93%), DT (92%), and Naïve Bayes (91%). These findings confirm the potential of machine learning for reliable DDoS detection while emphasizing the importance of proper model regularization to ensure generalizability. Future work should explore larger datasets, real-time traffic analysis, and hybrid models to further enhance robustness.
Design & Implementation of an IoT- Based Smart Robotic Vacuum Cleaner for Smart Home Environment
This paper presents the design and implementation of a smart robotic vacuum cleaner based on Internet of Things (IoT) technologies, named IO-VAC, specifically developed for smart home environments. The paper covers both the mechanical and electronic aspects of the robot’s development. A 2D layout was created using ArchiCAD to define the positioning of components and sensor angles when designing the robot, followed by a 3D model to visualize the final design. The robot chassis was fabricated using reinforced plastic to ensure durability and lightweight structure. The IO-VAC is controlled using an Arduino Mega board programmed in C/C++. Where, the system includes three ultrasonic sensors for obstacle avoidance, along with an infrared (IR) sensor for edge detection to prevent falls. The embedded system of IO-VAC integrates an ESP8266 Wi-Fi module, allowing for remote control via the Blynk IoT platform to enable users to control the robot’s movement (forward, backward, left, right), adjust suction power, select the desired cleaning algorithm, and monitor the battery level in real-time. In this paper three movement algorithms were developed: random, spiral and zigzag, and their performance was tested on two types of surfaces smooth and rough to ensure reliability and efficiency in various cleaning conditions after testing the validation of IO-VAC. Based on the test results of robot performance, the zigzag algorithm proved to be the most effective in terms of covered area, cleaning mission time, power consumption and minimizing redundancy. This work demonstrates an effective integration of embedded systems with IoT technologies, offering a smart and cost-efficient solution for automating household cleaning tasks.
Smart Home, IoT, Robotic Vacuum Cleaner, Arduino Mega, Spiral.
An Ensemble Learning-Based Approach for CBC Anemia Classification
Anemia is one of the most prevalent hematological disorders worldwide, affecting billions and leading to serious health complications. Early detection and accurate classification of anemia types are essential for effective treatment and management. This research proposes a machine learning-based framework for classifying nine distinct types of anemia using Complete Blood Count (CBC) data. The methodology integrates two feature selection techniques Recursive Feature Elimination (RFE) and heatmap correlation analysis to identify the most relevant biomarkers. A stacking ensemble classifier was developed using Random Forest, Support Vector Machine, K-Nearest Neighbors, and Decision Tree as base learners, with logistic regression as the meta-learner. Experimental evaluation showed that the (RFE) derived features yielded superior performance, the Anemia datasets that are available on Kaggle platform was used of which consists of 1,281 observations. The data was balanced to prevent the model from favoring the dominant class in the data. with the final model achieving an accuracy of 99.83%, and F1-score of 99.83%. The results demonstrate the effectiveness of ensemble learning and advanced feature selection in improving diagnostic accuracy and provide strong evidence for deploying intelligent decision-support systems in clinical settings. The generalizability of the proposed model was rigorously evaluated using 1,200 new, unlabeled CBC samples obtained from local laboratories in Misrata. In a blinded clinical validation research, the model’s predictions were compared against diagnoses made by expert hematologists, demonstrating strong concordance with an overall agreement of 84.67% across all anemia subtypes. These results underscore the effectiveness of ensemble learning combined with advanced feature selection.
Adaptive and Optimized RGB Channel Filtering for Noise Reduction in Color Images
This paper introduces an adaptive filtering method for color image denoising that employs Particle Swarm Optimization (PSO) to tune filter parameters. Building upon a standard mean filter baseline, the approach uses PSO to independently adjust coefficients for each RGB channel according to the specific characteristics of both the image and the noise present. The optimization process targets dual objectives: minimizing Normalized Mean Squared Error (NMSE) and maximizing Signal to Noise Ratio (SNR), while maintaining the core properties of mean filtering. Experimental validation using controlled Gaussian noise demonstrates substantial performance gains: NMSE decreased from 4.81% to 1.11%, and SNR improved from 13.18 dB to 19.55 dB, representing approximately four-fold enhancement over conventional mean filtering. The method shows particular promise for applications demanding image specific optimization, though its effectiveness varies with image content and noise characteristics.
Color Image Processing, Particle Swarm Optimization, Noise Reduction, RGB Filtering, Digital Image Enhancement, Adaptive Filtering.
AI-Driven Brain Tumor Segmentation: Review of the Last Decade
Brain tumors remain among the most difficult of the medical challenges, and the accurate and timely diagnosis is essential to achieve successful patient outcomes. Over the last decade, artificial intelligence (AI), and specifically deep learning, has profoundly transformed the paradigms of brain tumor detection and segmentation methodologies. This comprehensive review systematically examines the evolution of brain tumor segmentation AI models between 2015 and 2025, covering technological advancements, performance evaluation techniques, and challenges towards clinical translation. We follow the evolution from traditional machine learning approaches to sophisticated deep learning architectures, including Convolutional Neural Networks (CNNs), U-Net architectures, and the more recently emerged Vision Transformers (ViTs). It takes into account the most crucial performance metrics, i.e., Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), accuracy, sensitivity, and specificity, which are primarily tested against benchmarking datasets, such as BraTS. Our findings register noteworthy improvements in performance, wherein the top-performing current ensemble and transformer-based models deliver Dice scores well above 0.95 for whole-tumor segmentation. Despite the stunning progress, limitations in standardization of evaluation, model generalizability across clinical settings and interpretability persist. This review describes the critical views of current capabilities, shortcomings, and directions of AI-based brain tumor segmentation systems with focus on the road to strong clinical deployment of these systems.
Brain Tumor Segmentation, Artificial Intelligence, Deep Learning, Convolutional Neural Networks (CNN), Vision Transformers (ViT).
Performance Enhancement of Photovoltaic Panels with a Silicon-Based Backsheet: An Experimental Study
The electrical efficiency of the photovoltaic (PV) panels diminishes with rising cell temperatures, a key challenge in PV performance. While various passive cooling methods exist, there is a need for simple, integrated, and effective thermal management solutions. This study investigates the use of a silicon- based thermal isolator as a novel backsheet material to address this gap. A 0.25 mm Sil-Pad 400 sheet (Henkel) with a thermal conductivity of 0.9 W/m.K was laminated onto solar cells, replacing the standard tedlar layer. The performance of the modified panel was evaluated against an identical reference panel through a single-day, side-by-side comparative test. This protocol, employing synchronized measurements of electrical output and surface temperature from 11:00 to 14:00, ensured that both panels were subjected to identical environmental conditions, thereby normalizing the effect of solar irradiance fluctuations. Results confirmed the superior thermal regulation of the silicon isolator panel, which exhibited average temperature reduction of 4 °C on the front surface, and 2 °C on the back surface, yielding a combined average reduction of 3 °C. This effective cooling translated directly into a significant 13% average increase in power output. These findings demonstrate that silicon-based isolators are a highly promising solution for enhancing PV efficiency and energy yield, offering a practical and scalable approach for improving the performance of real-world solar installations.