Academy Journal for Basic and Applied Sciences

A Hybrid Neutrosophic Entropy-Based Multi-Criteria Decision Model with Application to Agricultural Plant Evaluation: plant in Abyan-Yemen

Madiha Abdullah Awbal Manduq — 2026-01-01

This study proposes a hybrid neutrosophic entropy based multi criteria decision- making (MCDM) model for evaluating agricultural plants under uncertainty. The model integrates neutrosophic logic with entropy weighting and is applied to six plant types in Abyan Governorate, Yemen. Statistical analysis using SPSS is conducted to validate the results. The findings demonstrate the robustness and reliability of the proposed model.

An Explainable Deep Learning Framework for Plant Leaf Disease Detection Using a Custom CNN Model

Sarah Shtawa — 2026-01-01

Agricultural sustainability relies heavily on the early detection of plant pathologies. However, manual diagnosis remains challenging even for experts. This study proposes a lightweight Custom Convolutional Neural Network (CNN) architecture for automated leaf disease detection. The model was evaluated against state-of-the-art frameworks, MobileNetV2 and EfficientNetB0, using a dataset of 15,649 images that integrates global data with locally sourced samples from Libya. To ensure robustness, k-Fold Cross-Validation was implemented under standardized conditions. The proposed Custom CNN achieved a competitive accuracy of 97.6%, closely matching EfficientNetB0 (98.4%). Despite the slight accuracy advantage of transfer learning models, the Custom CNN demonstrated superior computational efficiency and a significantly smaller architectural footprint. These results position the proposed model as an ideal candidate for deployment in resource-constrained environments and mobile-based diagnostic systems.

An Investigation of Risk Identification Methods in the Libyan Construction Industry

Abdulmuez Faraj Salim Almilyan — 2026-01-01

The Libyan Construction Industry (LCI) faces chronic challenges, including significant project delays and cost overruns, largely attributed to inadequate risk management practices. Effective Risk Identification is the foundational step for any successful risk management framework. This study investigates the current state of risk identification tools and techniques within the LCI, aiming to bridge the gap between theoretical importance and practical application. A mixed-methods approach was employed, utilizing a quantitative questionnaire to assess the perceived importance and actual application of 20 specific risk identification tools and techniques, supported by expert interviews. The findings reveal a significant disparity: while tools like Project Document Review and Use of Models and Software are rated as 'Very Important,' their application often lacks consistency, as indicated by high standard deviation values. The study highlights that the primary barrier to effective risk identification is not a lack of knowledge, but a weak organizational risk culture and insufficient commitment from senior management. The paper concludes by proposing a set of tailored recommendations to enhance the risk identification process, ensuring it is both comprehensive and contextually relevant for the Libyan environment.

An Investigation into Bias And Data Representativeness in Online Datasets

Hassan Najah — 2026-06-01

The rapid growth of publicly available online datasets has created new opportunities for machine learning research; however, these datasets are often sampled from populations that are unknown to the researcher. As a result, the processes used for data collection, labelling, and sampling are frequently undocumented, increasing the risk of unrepresentative or biased samples. When training data fail to reflect the underlying population, model outputs may propagate or amplify existing biases. This paper investigates the presence of bias in online datasets derived from different domains. The research aims to identify biases associated with the distribution of attributes which represent personal protected characteristics and to evaluate methods that mitigate these biases by improving data representativeness. Three datasets were selected to be part of the study, each containing at least one protected variable and enabling binary classification. The protected variables examined included marital status, race, and gender, respectively. Model performance was assessed using accuracy, sensitivity, and specificity. To investigate and address bias, data quality and representativeness techniques were applied, as well as bivariate statistical analysis to remove variables with no significant association with the class label. Initial results showed that accuracy alone provided a misleading picture of model performance, particularly when sensitivity was low and specificity was high. The results indicated that after applying the data representativeness and mitigation techniques, the models achieved a more balanced performance across all three metrics, despite slight reductions in accuracy and specificity. The findings highlight that accuracy alone is insufficient as a performance metric. When datasets are not representative, bias mitigation methods that balance sensitivity and specificity may reduce accuracy but lead to more equitable outcomes. Classification models perform more reliably when class distributions are balanced, and fair systems should ensure equitable accuracy, sensitivity, and specificity across all protected subgroups.

Analysing Failure Modes for Maintenance Strategies Development at South Tripoli Gas Turbine Power Plant

Osama Hassin — 2026-01-01

The reliable operation of power plants is essential for the continuity of modern societies. The South Tripoli Power Plant in Libya plays a crucial role within the national power grid. However, it faces significant challenges, including frequent operational failures, aging infrastructure, and ineffective maintenance practices, all of which compromise its reliability and escalate operational costs. This research is motivated by the urgent need to improve maintenance strategies in accordance with documented failure patterns, thereby establishing a comprehensive framework for effective maintenance planning. To address these challenges, a maintenance optimization framework has been developed that integrates Failure Modes, Effects, and Criticality Analysis (FMECA) with Fuzzy Logic techniques to enhance decision making processes regarding maintenance. Furthermore, a fuzzy FMECA framework has been designed to categorize maintenance strategies, thereby facilitating data driven maintenance planning. The contributions of this research are aimed at improving plant reliability, reducing the incidence of unplanned outages, and ensuring the sustainable performance of energy infrastructure.

Analysis of Stress Distribution in SHOFU Zirconomer using cdmHUB Estimates Against Experimental Cyclic Loading

MOHAMED ABOUR — 2026-01-01

This study investigates the predictive accuracy of the Composites Design and Manufacturing HUB (cdmHUB), a cloud-based micromechanics platform, in simulating the compressive behavior of Zirconomer. The primary objective is to determine if virtual "bottom-up" modeling can reliably predict fatigue failure and stress distribution as observed in experimental cyclic loading. The study utilized a dual-track approach:Computational Phase: We employed the Mori-Tanaka (MT) homogenization scheme on cdmHUB to calculate the effective elastic properties of a composite consisting of a polyalkenoate matrix (f=4.5GPa) reinforced with 10% volume fraction (Vf) zirconia particles (F=210 GPa). Structural analysis was conducted using the SwiftComp solver to map Von Mises stress distributions. Experimental Phase: Specimens of SHOFU Zirconomer (n=15) were fabricated (6x4mm cylinders) and stored for 24 hours at 37oC. Dynamic fatigue testing was performed using a Universal Testing Machine (UTM) at a frequency of 1.0 Hz for 500,000 cycles, with loads ranging from 50 N to 250 N. The cdmHUB simulation predicted a static compressive strength of 295.4 MPa, while experimental UTM results yielded a mean strength of 278.5 ± 12.3 MPa, representing a narrow deviation of 6.1%. Analysis of the Stress Distribution Map revealed that peak stresses were concentrated at the poles of the zirconia inclusions, aligning with the "interfacial debonding" failure modes observed in SEM micrographs of the experimental samples. However, a "deviation gap" was identified as zirconia content increased; at 20% Vf , the prediction error rose to 16.3%. This is attributed to the computational model's assumption of perfect particle dispersion, which contrasts with the physical agglomeration and micro-porosity typically encountered in high-viscosity hand-mixed cements. Furthermore, the S-N Curve demonstrated that while Zirconomer exhibits high static strength, its long-term endurance limit settles near 180 MPa, a critical threshold for clinical design. This research validates cdmHUB as a high-fidelity diagnostic tool for dental material prototyping. With a 94% accuracy rate for standard formulations, micromechanics simulations can effectively reduce the reliance on destructive testing during the initial stages of material design. Future iterations should incorporate time-dependent maturation kinetics to account for the dynamic chemical setting of glass ionomers.

Bacterial Contamination and Antimicrobial Resistance on Substantial-Touch Hospital Surfaces: A Cross-Sectional Study in Misrata, Libya

Masood Abdusalam Ghanem Ali — 2026-01-01

Hospital surfaces are important reservoirs for healthcare-associated infections (HAIs) and antimicrobial resistance (AMR), particularly in high-contact clinical environments. This study aimed to evaluate bacterial contamination, antimicrobial susceptibility patterns, and associated contamination risks in selected hospitals in Misrata, Libya. A cross-sectional study was conducted using 70 environmental surface samples collected from high-touch and low-touch areas. Standard microbiological methods and antimicrobial susceptibility testing based on CLSI guidelines were applied. Statistical analyses included chi-square tests, odds ratios, and logistic regression. The overall contamination rate was 77.1%, with the highest contamination observed on door handles, bed rails, and computer keyboards. Staphylococcus aureus was the predominant isolate (33.3%). Multidrug resistance (MDR) was detected in 41.5% of isolates, particularly among Gram-negative bacteria. Logistic regression indicated elevated contamination risk on high-touch surfaces. The findings highlight the critical role of hospital surfaces in pathogen transmission and AMR dissemination within healthcare settings.

Barium Sulfate–Cement Composites as a Sustainable Lead-Free Solution for Diagnostic X-Ray Shielding

Bsher Abour — 2026-06-01

The demand for non-toxic radiation shielding materials has increased due to environmental and occupational health concerns associated with lead-based barriers. Recent advances in composite materials have demonstrated that barium sulfate (BaSO₄) can serve as an effective attenuator for ionizing radiation, particularly in diagnostic energy ranges [1,3]. In this study, BaSO₄–cement composites were fabricated with varying thicknesses (0.2–1.0 cm) and evaluated under X-ray energies of 46 kV and 70 kV. The results revealed a significant decrease in transmitted dose with increasing thickness and BaSO₄ concentration, consistent with previous findings on composite shielding systems [2,21]. The 1 cm sample demonstrated the highest attenuation efficiency. In addition to performance, the study highlights the economic and environmental advantages of using locally available barite resources [5,20]. These findings support the feasibility of BaSO₄-based composites as sustainable alternatives to lead shielding.

CRISPR-dCas9-Mediated Epigenetic Reactivation of LRIG1 Suppresses Oncogenic Signaling in Triple-Negative Breast Cancer

Ali Ownis — 2026-01-01

CRISPR-dCas9-based epigenetic editing is emerging as a promising strategy for precise regulation of cancer-associated epigenetic alterations. Unlike wild-type Cas9, which induces double-strand breaks, dCas9 is generated by introducing D10A and H840A mutations that abolish nuclease activity while retaining sgRNA-guided DNA binding. This allows dCas9 to serve as a programmable scaffold for recruiting epigenetic effectors. Fusions with DNMTs, TET enzymes, KRAB, or transcriptional activators such as VP64, p300, and the SAM system enable targeted methylation, demethylation, or histone modification, thereby modulating gene expression without altering DNA sequence. A central focus is the tumor suppressor LRIG1, a regulator of growth factor signaling (e.g., EGFR), which is often silenced in basal-like and triple-negative breast cancers (TNBC) through promoter hypermethylation. CpG island methylation prevents transcription factor binding, reducing LRIG1 expression and contributing to oncogenesis. Similar repression occurs in colorectal and cervical cancers. By guiding dCas9-activator complexes to the LRIG1 promoter, transcriptional activators and chromatin-modifying domains can erase repressive methylation, promote histone acetylation, and restore transcription. This reactivation of LRIG1 has shown potential to suppress tumor proliferation in preclinical models. Beyond LRIG1, dCas9-based systems represent a versatile platform for reactivating tumor suppressors or silencing oncogenes with minimal off-target damage. Advances in delivery technologies and in vivo applications underscore their translational promise, although challenges remain, including chromatin accessibility and off-target recruitment. Overall, CRISPR-dCas9-mediated epigenetic editing offers a transformative and reversible framework for therapeutic gene regulation, providing high precision and adaptability for cancer treatment and broader applications in precision medicine.

Comparative Evaluation of CNN Architectures for Pneumonia Detection from Chest X ray Images

Khairia Mabrok — 2026-01-01

— Pneumonia remains a major global health burden, where timely recognition on chest X ray images is clinically important yet often challenged by subtle radiographic signs and variability in interpretation. This paper presents a controlled comparative evaluation of four convolutional neural network architectures, MobileNet, ResNet50, VGG, and InceptionV3, for binary classification of chest X ray images into diseased and normal cases. Experiments were conducted using a publicly available Kaggle dataset of 4,479 images under a unified preprocessing and evaluation protocol. Performance was assessed on a held out test set of 300 images, including 200 diseased and 100 normal cases, using accuracy and macro averaged precision, recall, and F1 score, supported by confusion matrix analysis. The results show that MobileNet achieved the highest test accuracy at 95.0 percent, while ResNet50 and VGG achieved 94.7 percent, and InceptionV3 achieved 92.0 percent. Confusion matrix inspection indicates that MobileNet produced the fewest false negatives for diseased cases in this setting, which is important for screening oriented use. Inference time measurements using batch size 1 at 180 × 180 input on CPU further highlight the efficiency advantage of lightweight architectures. Overall, these findings provide a reproducible benchmark to support architecture selection for computer assisted pneumonia screening and clinical triage .

Comparative Evaluation of Deep and Lightweight CNN Architectures for Multiclass Classification of Rickettsia Skin Rashes

ATIGAH E. KARNAF — 2026-01-01

Rickettsia diseases are frequently underdiagnosed due to nonspecific dermatological manifestations and the scarcity of publicly available annotated datasets. This study evaluates three pre-trained convolutional neural networks (CNNs)—ResNet-50, MobileNetV2, and AlexNet—for multiclass classification of Rickettsia skin rashes against six visually similar conditions: Chickenpox, Cowpox, Hand-Foot-and-Mouth Disease (HFMD), Healthy skin, Measles, and Monkeypox. A balanced dataset of 700 images (100 per class) was used, with five-fold cross-validation, data augmentation, and class weighting to mitigate data limitations and class imbalance. Results indicate that lightweight architectures, particularly MobileNetV2 and AlexNet, outperform deeper networks in detecting Rickettsia, achieving F1-scores of 93.14% and 93.88%, respectively, compared to 87.11% for ResNet-50. Ensemble prediction further enhanced stability and discrimination for rare classes. The findings suggest that computationally efficient CNN architectures provide a robust framework for early-stage screening of Rickettsia infections in low-resource clinical settings.

Comparative Seismic Performance of OMF Vs. IMF For Reinforced Concrete Residential Multi-storey Buildings in Tripoli, Libya

Lotfi O. Gargab — 2026-01-01

Abstract— Current construction practices in Tripoli, Libya, still depend on Ordinary Moment Frames (OMF) integrated with ribbed slab systems based on the traditional assumption of low seismicity in the region, however, recent local seismic hazard studies have classified the region Seismic Design Category (SDC) as C or D, which necessitates a critical assessment under the current structural norms. The seismic adequacy of an eight-story reinforced concrete (RC) building was investigated under three different configurations based on American Concrete Institute (ACI 318-19) and American Society of Civil Engineers (ASCE 7-16): conventional OMF with ribbed slabs, Intermediate Moment Frames (IMF) with ribbed slabs, and IMF with solid slabs. The standard OMF-ribbed configuration was analyzed and found to have 23.4% of the beams failing due to critical shear-torsion failures, which is fundamentally not adequate for current demands, while upgrading to an IMF-ribbed system reduced the failure rate to 10.5% (55% improvement) that is still not compliant with the code, and the IMF with a solid slab system achieved 100% structural adequacy with no beam failures, inter-story drift of 0.57%, and column demand-capacity (P-M) ratios within a safe threshold of 0.68. These localized findings suggest that current construction practices may underestimate seismic vulnerability, and consequently, implementing an IMF integrated with a solid slab system provides an enhanced structural configuration to satisfy modern code provisions and warrants consideration in future revisions of local construction regulations.

Comparison of NDVI Time Series between MISBAR and GEE Platforms: A Case Study of a High-Yield Wheat Field during the 2022/2023 Season

Samira Sultan Dhafer Almuzoghi — 2026-01-01

Abstract— In this study, the most widely used vegetation index, the Normalized Difference Vegetation Index (NDVI), was used to extract NDVI time series data for a high-yield wheat field during the 2022/2023 season from two cloud computing platforms: MISBAR and Google Earth Engine (GEE), it was observed that although the MISBAR platform provides an option to filter satellite imagery based on a maximum cloud cover threshold, it calculates its indicator values without actually applying this condition. Therefore, temporal alignment was applied by selecting only the overlapping dates between the two platforms before performing the statistical analysis. Comparing the mean NDVI values from both platforms revealed that they are temporally and spatially consistent in tracking the growth stages of the wheat crop, with a correlation coefficient of 0.998 and a positive bias toward the GEE platform. A linear relationship was found between them, where the MISBAR platform tended to calculate lower NDVI values than the GEE platform, while both maintained a similar temporal trend throughout the crop growth stages.

Comprehensive Assessment Of Voltage Stability And Security Enhancement In The Libyan High-Voltage Grid

Salma Belal Ali — 2026-06-07

This research proposes an integrated framework for the static and dynamic security assessment of the Libyan 220/400 kV transmission infrastructure. The study investigates system resilience through N-1 contingency analysis and bifurcation-based voltage stability evaluation using PV and QV curves. By employing Performance Indices (PI) for criticality ranking, the analysis quantifies the impact of severe contingencies under diverse loading conditions (5523 MW and 6200 MW). Furthermore, a sensitivity analysis is conducted on generator dispatch strategies and Automatic Voltage Regulator (AVR) configurations to identify optimal security margins. Dynamic simulations highlight oscillatory stability boundaries and the proximity to voltage collapse. Finally, the study validates strategic reinforcement measures—including 400 kV infrastructure upgrades—using NEPLAN, demonstrating a significant enhancement in the grid’s operational reliability.

Design of Perpetual Highway Pavement in Libya Using PerRoad softwaer: Solutions for Maintenance Gaps and Traffic Overload

Jamal Abdulah Beitelmal — 2026-06-07

This study aims to identify solutions for the early failure of roads in Libya due to the lack of a pavement management system and an effective load weighing system. The coastal highway in the city of Misrata was taken as a case study, which has high traffic volumes, especially heavy vehicles. The companies involved in the road's reconstruction provided data on average yearly temperatures and the physical properties of the construction materials, This included the modulus of elasticity and Poisson's ratio for both the asphalt mix and the soil used, The design for traffic volumes was based on ESALs reaching 24 M.S.A which is the calculated value for this road. , the ESALS were increased by 5 M.S.A and designed by using PerRoad program, for perpetual pavement method, and the mechanical experimental method. After the design, both methodologies were compared, and the effect of increasing the number of axles and ESALs on the thickness of the asphalt layers was studied, The results showed that the design of this road using the perpetual pavement method is not affected by the increase in the number of axles and ESALs, which is suitable for achieving a design life of up to 50 years without being affected by the increase in ESALs. The asphalt layers were 25cm thick, while the subgrade layer was 20 cm thick, On the other hand, the mechanical experimental showed a clear impact from the increase in the number of axles and ESALs, as the thickness of the asphalt layer increased from 19 cm to 28 cm, and the thickness of the subgrade layer increased from 30 cm to 40 cm. This design achieves a lifespan of 20 years. The study also indicated that for designs with 15 M.S.A and above, the traditional design is considered over-designed, and the use of permanent pavement is preferred. As for the design of this road, the permanent pavement method was adopted

EfficientNetB0-Based Lightweight AI System

Mohanned Naji Albibas — 2026-01-01

Lettuce (Lactuca sativa) is critical for Libyan food security and smallholder income, but production is severely affected by fungal, bacterial, and viral diseases. Conventional diagnosis relies on subjective, time-consuming expert inspection, often unavailable in resource limited rural areas. This paper presents a lightweight Artificial Intelligence (AI) system for real time lettuce disease diagnosis, tailored to the Libyan agricultural context.

Establishing a Pavement Management System for a Segment of the Road Network in Tripoli

Jamal Abdullah Beit El-Mal — 2026-01-01

One of the major challenges facing road networks, particularly in developing countries, is the premature deterioration of pavement structures caused by excessive traffic loads, temperature fluctuations, and inadequate attention to periodic road maintenance. The city of Tripoli represents a clear example of these challenges, as its road network suffers from several issues that require the adoption of modern scientific approaches to improve maintenance management efficiency and enhance the level of service provided to road users. This paper aims to present a scientific framework for establishing a Pavement Management System (PMS) to support future maintenance planning for the road network in Tripoli. The proposed framework is based on the collection and analysis of pavement condition data using recognized evaluation indices, integrated with decision-support mechanisms. This approach contributes to improving monitoring and follow-up processes, prioritizing future maintenance activities, optimizing the utilization of available resources, and extending the service life of the road network. Furthermore, the paper discusses the anticipated impacts of implementing a Pavement Management System and its role in enhancing the efficiency of road infrastructure management.

Environmental Assessment of Refinery-Derived Hydrocarbon and Trace Metal Contamination in Coastal and Groundwater Systems: A Case Study from Zawia, Libya

Laila Rtemi — 2026-01-01

Oil refining activities can represent a persistent source of hydrocarbon contamination in coastal and groundwater environments, particularly in regions where long-term monitoring data are limited. This study presents an integrated environmental assessment of refinery-derived hydrocarbon and trace metal contamination in offshore, shoreline, and groundwater systems surrounding the Zawia Refinery, western Libya. Twenty samples were collected from offshore waters, shoreline zones, and groundwater wells and analysed for total petroleum hydrocarbons (TPH), chemical oxygen demand (COD), dissolved oxygen (DO), total dissolved solids (TDS), pH, and selected trace metals. The results show strong spatial variability in contamination, with offshore stations AS7 and AS8 exhibiting the highest TPH concentrations (up to 43 mg/L). Groundwater wells AG2 and AG6 also showed elevated TPH, accompanied by pronounced oxygen depletion, indicating strongly reducing conditions in the most impacted wells. Correlation analysis identified a strong positive relationship between TPH and COD and a significant negative relationship between TPH and DO, consistent with oxygen consumption during microbial degradation of petroleum hydrocarbons. Nickel and vanadium enrichment in the most contaminated groundwater wells further supports refinery-related source signatures. Overall, the findings indicate both horizontal dispersion in marine waters and vertical migration into the shallow aquifer, emphasizing the need for integrated monitoring and targeted mitigation in refinery-impacted Mediterranean coastal settings.

Evaluation of the Algorithmic Decision Making in healthcare and public administration areas - A systematic literature review

Hend ALmezoghy — 2026-01-01

The rapid changes in the fields of artificial intelligence (AI) and big data analytics have transformed an algorithmic decision-making system (ADMS) with considerable uses in the government sector and the medical field. Nevertheless, there is growing interest in the literature on ADMS that is still scattered across disciplines and offers conflicting findings regarding its effectiveness. The socio-technical relationship of ADMS, the ethical issues, and organizational impact are complex, yet they are not comprehensively studied, even though the use of ADMS grows. The study will provide a thorough review of the literature that will bring together interdisciplinary viewpoints on the creation, adoption, and impacts of ADMS in these essential sectors. The paper integrates both empirical and theoretical sources to explain such key considerations that affect algorithmic judgments, including accuracy, fairness, transparency, human oversight, and governance systems, based on a comprehensive search and rigorous selection process. The findings provide inconsistency in efficiency gains, transfer of power, and dehumanization-anthropomorphism dialectic in the organizational setting, which presents significant contradictions between technological efficacy and socio-ethical issues. The paper focuses on the need to have balanced approaches to ensure that human agency is safeguarded as algorithmic abilities are used, extending theoretical concepts of fairness beyond measures of accuracy to consider equity and legitimacy. This synthesis is informative of future research activities focused on conducting empirical validation, assessing harm, adaptive governance, and inclusive design. Ultimately, the work contributes to the understanding of the interdisciplinary character of ADMS and gives essential guidance towards moral, accountable, and just algorithmic decision-making in high-stakes areas.

Feature selection using optimization algorithms in heart disease detection

Jazya Amshaher — 2026-01-01

Heart disease continues to be among the leading causes of mortality worldwide, necessitating its early detection for improving patient prognosis. Although conventional statistics-based algorithms and feature selection approaches have been applied for heart disease prediction purposes, the complex nature of medical data may not necessarily be accounted for using such techniques. Removing unnecessary features improves how well machine learning algorithms perform. In this study, we present a step-by-step feature selection methodology leveraging Genetic Algorithms (GA) and the Gini index. Utilizing greedy search for parameter tuning and and a comprehensive suite of confusion matrix metrics for evaluation, we compared six architectures: MLP, TabNet, Random Forest, SVM, Logistic Regression, and XGBoost are applied to the heart disease dataset. Our results demonstrate that the Random Forest (RF) model delivers superior diagnostics. By reducing the feature space from 13 to 8, the RF model attained exceptional predictive power, charting 98.4% accuracy, 98.9% sensitivity, 98% specificity, and a 98.5% F1-score.

Impact of Grid-Connected Photovoltaic Distributed Generation on a 66 kV Sub-Transmission Network in Southern Libya: A Case Study

Walid Amar Alaromi — 2026-01-01

This paper investigates the integration of a 5 MW photovoltaic (PV) distributed generation (DG) system into the 66 kV Tragan sub-transmission network in southern Libya, a weak grid with poor voltage performance and high reactive power demand. The PV system is sized using PV*SOL, while load flow and contingency analyses are carried out using NEPLAN. The results show that PV-DG significantly improves network performance. Real power losses are reduced by 30.6% (from 3.501 MW to 2.429 MW), while the minimum bus voltage increases from 90.4% to 97.8%, eliminating all five voltage violations. Under N-1 contingency conditions, the minimum voltage during transformer outage improves from 87.2% to 94.2%, satisfying operational limits. The analysis also shows that PV alone cannot fully support the evening peak, and a 3 MW/6 MWh battery is recommended. Furthermore, an economic assessment based on actual generation cost gives a payback period of 6.5 years, and a negative carbon abatement cost (–$149/tCO₂). Environmentally, annual CO₂ emissions are reduced by 4,813 tons. Overall, the study confirms that PV-DG is a practical and effective solution for improving weak desert networks such as Tragan, although battery storage, transformer upgrades, and proper protection coordination are needed to achieve full system reliability.

Multi-Objective Optimization Based Group Recommender System

Wesam Abdalla ALnajjar — 2026-06-01

Most existing studies in Group Recommender Systems (GRS) explore fairness and diversity independently. It is challenging to consider the feasibility of simultaneously incorporating fairness and diversity in Group Recommender Systems. This paper examines their quantitative relationship and proposes a framework that integrates fairness and diversity within the recommendation process. The method clusters users into subgroups based on their preferences, creates pseudo-users to represent these subgroups, and aggregates the recommendations to form a final group recommendation list. The aim is to enhance the fairness and diversity of recommendations. This design aims to balance user-level fairness and diversity while maintaining accuracy. Experimental results on the MovieLens dataset show that the proposed method outperforms the baseline method across different evaluation metrics, demonstrating the importance of simultaneously optimizing fairness and diversity in group recommender systems.

Optimizing Planting Date Enhances Growth and Flowering of Broad Bean (Vicia faba L.) under Mediterranean Greenhouse Conditions

Masood Abdusalam Ghanem Ali — 2026-01-01

This study evaluated the influence of planting date on vegetative growth and flowering performance of broad bean (Vicia faba L.) under semi-controlled greenhouse conditions in Sirte, Libya. A completely randomized design (CRD) was used with three sowing dates (15 February, 15 March, and 15 April 2024). Although the experiment included a limited number of replicates (n = 3), strict environmental control and uniform management practices were applied to enhance data reliability. Average greenhouse temperatures varied across planting periods, ranging from 12–18 °C in February, 15–25 °C in March, and 20–32 °C in April, thereby exposing plants to progressively increasing thermal conditions. These temperature differences provided a practical framework for evaluating plant responses to moderate and elevated temperature regimes. Results showed that planting date significantly affected plant growth and reproductive performance. Two-way ANOVA revealed significant effects on stem length (F = 18.4), leaf number (F = 12.7), and number of flowers (F = 21.5) (p < 0.05). Early planting (February) enhanced vegetative growth, as reflected by increased leaf production and expansion, whereas mid-March planting resulted in the highest flowering performance (13.5 ± 1.2 flowers plant⁻¹). In contrast, April planting, which coincided with higher temperature ranges exceeding 30 °C, led to significant reductions in flowering (6.0 ± 1.5 flowers plant⁻¹), suggesting the onset of heat-induced reproductive limitations rather than optimal growth conditions. Overall, the findings demonstrate that planting date influences the balance between vegetative and reproductive development in broad bean by altering plant exposure to temperature conditions. Under Mediterranean greenhouse environments, mid-March planting provided the most favorable thermal window for optimizing reproductive performance.

Psychological Fears and Their Association with Closed Magnetic Resonance Imaging (MRI)

Liala T. Elessawi — 2026-01-01

This study aims to analyze the psychological fears associated with Magnetic Resonance Imaging (MRI), given their direct impact on patient experience, medical image quality, and examination compliance rates. The research adopted a descriptive-analytical approach, involving a review of relevant literature and the distribution of a questionnaire to a simple random sample of 50 individuals in Tripoli, Libya, in December 2023. The results showed that the percentage of male participants was 60% (30 individuals), while the percentage of females was 40% (20 individuals). The most represented age group was 55-65 years, accounting for 26%. It was also found that the most common educational qualification was university education at 40%, and 90% of the sample members had previously undergone an MRI scan. Furthermore, 70% of participants indicated feeling fear during the examination, with the most common symptom being fear of harm (50%), followed by fear of dark places (40%). Some fears were also linked to childhood experiences.

Recording a new variety of date Palm in the Libyan coastal region

Abobakir Ali Elhaj — 2026-01-01

Abstract: Date palms are characterized by morphological and genetic variation among their varieties. The emergence of new date palm varieties results from genetic segregation during embryonic development following fertilization and subsequent seed formation. Through seed propagation, several new varieties appear annually, identified when their fruits exhibit unique characteristics that grant them high aesthetic, nutritional, and market value. This descriptive study led to the discovery of a new variety, named "Al-Raqi," in the Al-Jadida area of Al-Ajilat, located on the western coast of Libya. This discovery followed an evaluation of its vegetative, floral, fruit, and seed characteristics, as well as some of the fruit's nutritional components. It was found that the morphological and color characteristics of the fruit, particularly during the Khalal (unripe) and Rutab (ripe) stages differ from those of known local varieties, confirming its distinctiveness and originality. The discovery of this new variety represents a significant addition to Libya's heritage of date palm varieties. The necessary procedures have been initiated for its official registration and documentation with the relevant authorities.

Perfect and Near-Perfect Matchings in Bilinear Congruence Graphs over Z_n

Hamza A. Daoub — 2026-06-01

We study matching and covering parameters of the bilinear congruence graph over the ring . The graph has vertex set , and two distinct vertices and are adjacent whenever . This adjacency is a determinant-zero condition, so it describes modular dependence between ordered pairs rather than the usual product-zero relation from zero-divisor graphs. We prove that, for every odd integer , the map gives a near-perfect matching of .

Overcoming the Curse of Dimensionality in Microarray Data Via Generative Data Augmentation and L1-Regularized Selection

Fawzia A. E. Mansur — 2026-06-01

Feature selection research is crucial to overcome the dimensionality curse. Despite numerous attempts to select features for high-dimensional data, many existing techniques suffer from limited computational efficiency and fail to capture complex and deep correlations among features, particularly in highly complex and low-sample-size. This article presents an innovative framework that uses conditional competitive generative networks (CGANs) to mitigate the limitations of differential evolution (DE)

and the low-dimensional, small-scale dataset problem (HDLSS) by increasing dataset size and improving the stability of the selection process, thereby enhancing the model's generalizability. Experimental results show that the model achieved a classification accuracy of 89.59%, surpassing the best reference method of 86.10%. This highlights the framework's effectiveness in balancing accuracy and computational efficiency, as well as its applicability to diverse high-dimensional data scenarios.

Paper Debugger: An Editor-Native Multi-Agent Framework for Agentic Academic Writing in Overleaf

Nureddin Ali Aldali — 2026-06-01

Large Language Models (LLMs) have transformed academic writing, yet existing tools operate as external applications that disrupt cognitive flow through repetitive copy-paste cycles. This fragmentation prevents AI systems from understanding document structure, citation dependencies, and LaTeX-specific context. We present Paper Debugger, an editor-native multi-agent framework integrated directly into Overleaf via a Chrome extension. Leveraging Kubernetes orchestration and the Model Context Protocol (MCP), Paper Debugger deploys specialized agents for grammar refinement, structured critique, citation verification, and literature retrieval—while maintaining full revision transparency through deterministic diff-based patches. In a pilot study with 25 researchers, the system reduced manual formatting time by 34% (p < 0.05) and achieved a System Usability Scale (SUS) score of 78.4, indicating above-average user satisfaction. Unlike traditional external tools, Paper Debugger preserves workflow continuity and enables contextual, structure-aware AI assistance within the native authoring environment.

Improving the Performance of Polar Decoders Using Virtual Erasure Channels

Abdelkareim Alrtaimi — 2026-06-01

In this paper we propose the use of virtual erasure channels (VECs) to improve the performance of successive cancelation decoding algorithm used to decode polar codes. VECs are concatenated to the continuous channels such as additive white Gaussian noise (AWGN) or Rayleigh channels. The outputs of the continuous channels are passed through VECs before decoding operation. The inputs of the VECs falling into a threshold interval are erased. We also provided techniques to determine the threshold levels of VECs. For the computer simulations binary phase shift keying (BPSK) is used. It is seen from computer simulations that the proposed method significantly outperforms the performance of successive cancelation polar decoders.

Factors Influence Cross-Prompt Scoring of Arabic Essays

Ahmed Ibrahim Suleiman — 2026-06-01

Cross-prompt automated essay scoring (AES), which involves training on specific prompts and testing on unseen cases, represents a practical application scenario; however, this area remains underexplored for Arabic. Progress in AES is typically characterized by a succession of neural architectures that exhibit escalating levels of complexity. However, such progress is seldom evaluated against more basic choices such as tokenization, model-selection criteria, and text segmentation. We present the first controlled factorial study of cross-prompt Arabic AES on the LAILA dataset, crossing three architectures of increasing complexity (flat, hierarchical, multi-trait), three tokenizer–model configurations, and three random seeds, and benchmarking against a strong prompt-agnostic feature baseline. Our central finding is that tokenizer configuration drives performance far more than architecture: it outperforms each architectural step by approximately four times, covering a range of 0.16 to 0.19 in the overall Quadratic Weighted Kappa (QWK), the agreement metric used throughout. By contrast, the move from a flat to a hierarchical encoder yields only a small and seed-fragile gain (+0.045), multi-trait attention is mildly harmful (−0.02), and flat AraBERT matches the feature baseline (0.621 QWK) — which itself outperforms the hierarchical and multi-trait models in four of six configurations. We additionally provide evidence on how a seemingly straightforward "more-complex-is-better" result (M2 = 0.637 vs. M1 = 0.556 in our own early runs) is an artifact that dissolves once tokenization, selection criterion, and segmentation are each controlled. We release a corrected evaluation protocol — mean-per-prompt checkpoint selection and a sentence-based segmenter suited to Arabic — and argue that such controls are prerequisites for credible architecture claims in low-resource cross-prompt AES.