Wadi Alshatti University Journal of Pure and Applied Sciences
Wadi Alshatti University Journal of Pure and Applied Sciences (ISSN: 3006-0877) is a peer-reviewed, open-access academic journal dedicated to the dissemination of high-quality research in the fields of pure and applied sciences. Published biannually (two issues per year), the journal serves as a platform for researchers, scientists, and academics to share innovative findings and advancements in disciplines such as: Physical Sciences (Physics, Chemistry, Earth Sciences) Biological Sciences (Biology, Microbiology, Biochemistry) Mathematics & Statistics Environmental Sciences Computer Science & Information Technology Engineering & Technology (where applicable) Journal Mission The journal aims to promote scientific knowledge by publishing original research articles, reviews, and short communications that contribute to theoretical and practical developments in pure and applied sciences. It upholds rigorous peer-review standards to ensure the validity, significance, and originality of published work. Key Features Open Access: All articles are freely available to readers worldwide, ensuring broad dissemination of knowledge. Peer-Reviewed: Manuscripts undergo a double-blind review process by experts in the relevant fields. ISSN: Online ISSN 3006-0877 Frequency: Two issues per year (biannual). Indexing & Archiving: The journal is committed to being indexed in reputable databases to enhance visibility and impact. Article Processing Charge (Authors' Fees) This journal charges no authors' fees Article Submission Charge: 0.0 LD Article Processing Charge (APC): 0.0 LD WAUJPAS is run on a non-profit basis. Wadi Alshatti University covers 100% of the expenses of publishing Publisher The journal is published by Wadi Alshatti University, supporting the institution’s commitment to advancing scientific research and education in Libya and beyond. For inquiries, please contact the Editorial Office at [inf.wuj@wau.edu.ly].
This journal uses Creative Commons Attribution-Noncommerical 4.0 International License (CC BY-NC 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/. Copyright of articles Authors retain copyright of their articles published in this journal.
Effects of Long-Term Cultivation on Soil Physicochemical Properties and Heavy Metal Accumulation
This study evaluated the effects of long-term cultivation on the physicochemical properties of soil, including pH, organic matter (OM), electrical conductivity (EC), cation exchange capacity (CEC), and concentrations of some heavy metals (HMs) such as Cr, Cu, Cd, Mn, Zn, Ni, Fe, and Pb of Open Farm (OF) soils. A total of 210 soil samples were collected from six depths (0-60 cm). The findings revealed slight alterations in physicochemical properties resulting from cultivation and chemical fertilizers. Heavy metal concentrations in OF soils were below maximum permissible limits, but Cu, Cd, Mn, and Zn accumulated compared to uncultivated soils. The heavy metals pollution index (HPI) indicated low pollution for Fe in OF soils (PI ≤ 1), with HPI values decreasing with increasing soil depth. The Geoaccumulation index (Igeo) indicated that Open-farm soils were uncontaminated or moderately contaminated with Mn. A strong positive correlation between physicochemical characteristics and heavy metals in OF soils suggests a common pollution source, whereas negative correlations indicate different pollution sources. Principal component analysis (PCA) identified three major components in soil, each associated with specific heavy metals. The study indicated that Cd, Cu, and Zn originated from agricultural activities, while Cr, Ni, Fe, and Mn were derived from natural soil-forming processes.
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Effect of Ambient Temperature and Fuel Switching on the Performance and the Environmental Impact of Gas Turbine Plants: A Case Study of West Tripoli Gas Power Plant
This paper analyses the performance and environmental impacts of gas turbine plants operating on diesel and natural gas. It provides a comprehensive assessment of how varying ambient temperatures and fuel type affect gas turbine operation and pollutant emissions. This is done under identical turbine inlet temperatures and net power to eliminate their effects and to enable a clearer comparison. Based on a case study of the Tripoli West 167 MW simple-cycle power plant, a thermodynamic model developed in Excel is used to simulate GT performance. Results indicate that as ambient temperature changes, net exergy destruction is primarily influenced by the exergy destruction changes in the turbine (~45%) and the compressor (~40%), although the combustor is responsible for over 80% of the net destruction in the cycle. The results also highlight the relationships between specific fuel consumption, net thermal efficiency, and pollutant emissions. For every 10 °C rise in ambient temperature, the net thermal efficiency declines by 1.55% and 1.5%, the specific fuel consumption rises by 1.57% and 1.52%, and pollutant emissions rise by 1.58% and 1.53% for diesel and natural gas, respectively. This may link the changes in net thermal efficiency and pollutant emissions to the specific fuel consumption. Switching from diesel to natural gas significantly reduces net exergy destruction (~73%), attributed to reduced combustor exergy destruction, and boosts overall thermal efficiency by about 1%. Switching to natural gas also reduces CO2 emissions by (~24%) due to a lower carbon-to-hydrogen ratio (~15.2%) and lower fuel consumption (~10.2%). The emission factors determined by this study are 3.082 kg CO2/kg fuel for diesel and 2.612 kg CO2/kg fuel for natural gas. The change in emission factor between diesel and natural gas is similar to the change in carbon-to-hydrogen ratio between the two fuels (~15.2%). This relates the CO2 emission factor to fuel chemistry. The findings suggest that using natural gas is preferable, especially in warmer months.
Copyright (c) 2026 The authors https://creativecommons.org/licenses/by-nc/4.0
Higher-Order Recurrent Finsler Geometry and Anisotropic Flow Modelling with CFD Applications
In this work, a unified high-level framework that links anisotropic flow via higher-order recurrence structures with Finsler geometry is developed. It focuses on the connections between Weyl projective and concircular curvature tensors in extended second-order recurrent and birecurrent Finsler spaces. By obtaining new tensorial relations and strict necessary and sufficient conditions, classical recurrence theory is expanded. To confirm applicability, CFD simulations of viscous flow in curved pipes with varying curvatures and intake conditions are performed. The results demonstrate significant directional dependency in velocity and wall shear stress. The results are consistent with the proposed geometric model, indicating a close relationship between advanced differential geometry and anisotropic fluid behavior with implications for modified gravity and cosmology.
AI-Enhanced Reservoir Storage Assessment and Depletion Forecasting Using Multispectral Satellite Imagery and Hydrological Modeling
The study suggests a novel AI-based approach for water-storage estimation in reservoirs and for forecasting depleted water levels, using multispectral data from satellites and hydrological models. Sentinel-2 data observed over Haditha Lake (Iraq) on 7 September 2025 was processed with machine-learning and deep-learning algorithms for water surface delineation and water level and water depletion forecasting were applied using predictive models. For the water-surface classification, the U-Net model was tested with an overall accuracy of 96.8%. For the water-level prediction, the predictive model used was an ensemble of water-level predictive models, whose water-level prediction error (RMSE = 0.19 m) was smallest among the models evaluated. Based on the AI derived evaluation, current reservoir storage is estimated to be around 2.80 billion m³ or equivalent to 25.2% of the original reservoir storage. The flow conditions that were evaluated (inflow: 194 m³/s; outflow: 375 m³/s) resulted in a remaining time of 5.9 months (with a 95% confidence interval of 5.6–6.2 months) according to the depletion-prediction model. The proposed framework illustrates how satellite-image analysis, predictive modeling and uncertainty quantification can be combined for monitoring and decision making on reservoirs. The estimates of storage and depletion, however, should be considered model-based and should be further validated based on independent field observations.
Copyright (c) 2026 The author https://creativecommons.org/licenses/by-nc/4.0
The Impact of Kahoot Gamification on IT Project Management Learning in Libyan Higher Education
This study aims to investigate the effectiveness of using gamification on Kahoot to enhance learning in Information Technology Project Management course, boost student engagement and motivation, raise academic achievement, and improve attitudes toward learning. The importance of this study is highlighted by the integration of educational games into learning to enhance student participation, improve cognitive skills and attitudes toward learning, motivation, and academic achievement. In addition, it explores the challenges facing the implementation of gamification at Information Technology Faculty of Alasmarya Islamic University (AIU), Libya. The study employs a mixed-methods approach that combines quantitative and qualitative methods to investigate the effects of gamification tools on student engagement. The study framework includes two phases carried out during the 2024/2025 academic year: the first phase used the gamification platform, Kahoot, with lectures conducted throughout the semester. The second phase took place at the end of the semester, after the completion of Project Management course topics. Students in the experimental and control groups underwent a pre-test and post-test, along with pre- and post-intervention questionnaire, and semi-structured interview was conducted with the experimental group only. The results from previous studies, along with this study's results, indicate that game-based learning significantly increases student engagement, fosters motivation, boosts self-confidence, and improves learning outcomes in IT Project Management course. This study faces challenges, including the incompatibility of students' devices with Kahoot in the classroom. Therefore, the integration of gamification is necessary for enhancing the quality of the educational process and preparing graduates capable of working effectively in a technology project environment.
The Role of Spatial Technologies in Modeling Changes in Green Spaces in the City of Tobruk: A Study in Applied Geography
This study aims to highlight the role of remote sensing technologies. The study utilized RS and Geographic Information Systems (GIS) to understand the current state of green spaces in Tobruk, reveal changes in these spaces between 2013 and 2025, and assess the effectiveness of these modern technologies in analyzing and monitoring green spaces using the NDVI spectral index. It also aimed to produce spatial and temporal maps and model them cartographically by comparing different classifications using the Change Detection tool. The results showed that the NDVI reached its highest value in 2013 at 3.37%, and in 2025 it reached 3.93%. This indicates that vegetation cover is unstable, due to the nature of the climate, particularly fluctuating rainfall rates, and the continuous urban expansion driven by population growth. the study concluded that green spaces appear in some locations, particularly in the valley corridors extending from east to west in the northern part overlooking the Mediterranean Sea, and some of them are distributed in the south eastern part. Through the use of the developed tool and the values obtained from the NDVI spectral index, it was shown, according to the description of the change, that there is a severe decrease between 2013-2025 AD by an area of 0.37 km² of the total area of the city, while there is a severe increase in the density of vegetation cover by an area of 0.49 km². The study recommended the need to pay attention to remote sensing and geographic information systems technologies, and employ them in affiliated institutions in the field of environmental monitoring, due to their efficiency in monitoring the environment, their speed in implementation, and their high accuracy in giving results, according to the desired goal.
Analysis and Comparison of Resonant Compensation Topologies in Wireless Power Transfer Systems with Rectangular Planar Coils
Wireless power transfer (WPT) based on resonant magnetic coupling is a promising technology for electric vehicle (EV) charging. This paper presents an analytical and comparative study of four fundamental compensation topologies—Series–Series (SS), Series–Parallel (SP), Parallel–Series (PS), and Parallel–Parallel (PP)—using rectangular planar transmitter and receiver coils. The rectangular geometry was selected for its suitability for ground-mounted EV charging pads and efficient use of installation space. Unlike previous studies that mainly focused on circular coils or limited topologies, this work provides a unified comparison of the four topologies under identical operating conditions. MATLAB simulations were conducted to evaluate the effects of air-gap distance on magnetic coupling, transferred power, and system efficiency. The coupling coefficient decreased from 0.4259 at 100 mm to 0.3337 at 200 mm, while mutual inductance decreased from 5.568 µH to 4.365 µH. At 100 mm, the SP topology achieved the highest load power of 14.64 W, whereas the PS topology achieved the lowest value of 4.26 W. The PP topology exhibited the highest efficiency of approximately 63%, while the SS topology remained below 40%. These results indicate that SP and PP are promising compensation topologies for medium-coupling WPT systems, particularly for EV wireless charging applications.
Geological Framework and Exploration Potential of Manganese Mineralization in Libya: A Comparative Study of Nalut–Jabal Nafusa, Wadi Urari and Shati Valley
A conceptual prediction framework for assessing the potential for manganese exploration in three important areas—Nalut–Jabal Nafusa, Wadi Urari, and Shati Valley—is presented in this paper. Geological mapping, remote sensing investigation, structural analysis, mineral information, and spatial evaluation of regulating factors impacting manganese mineralization are all included into the methodology. The Nalut–Jabal Nafusa region exhibits potential for fault-controlled mineralization and manganese deposits supported by carbonate. An interesting site connected to structural pathways and sedimentary deposits on the edge of the Ghadames Basin is Wadi Urari. Due to local tectonic features regulating mineral accumulation and iron-bearing Carboniferous strata, the Shati Valley region exhibits considerable promise. The obtained results can serve as an initial guide to the areas of priority exploration, mineral assessments, and geochemical surveys. In addition to the potential mineralization sites, sustainable manganese production can also help Libya achieve economic diversification through processing, creating jobs, and contributing to critical mineral supply chains for hydrogen-based energy systems. However, detailed research, environmental impact assessments, and economic evaluations are required before any significant commercial production can commence.
Analysis of Wheat Crop Growth Using Sentinel-2 Satellite Data and NDVI in Al-Dafnia, Misurata, Libya during the 2022-2023 Agricultural Season
This study aims to monitor the growth dynamics of wheat crops in Al-Dafnia region, Misurata Libya, during the 2022-2023 agricultural season using Sentinel-2 satellite data. Remote sensing technologies, specifically NDVI (Normalized Difference vegetation Index), were used to evaluate the vegetation status across different growth stages from planting to harvesting. Images from Sentinel-2 were processed using ArcGIS. With a custom Model Builder workflow to automate NDVI calculation and extract temporal data. The results revealed a gradual increase in NDVI values during the vegetative stages, peaking during elongation, followed by noticeable decline during the maturity stage. This reflects the potential of NDVI in estimating crop health and development. The study emphasizes the efficiency of combining satellite data with GIS tools to achieve, timely crop monitoring and recommends expanding the methodology to include other crop types and environmental variables for improved decision – making in agriculture.
Wireless Sensor Networks in Desert Environments: A Systematic Review of Challenges, Solutions, and Research Gaps
Observing ecosystems in desert zones is far from simple. Hostile weather patterns combined with limited infrastructure complicate the process of structured environmental monitoring. Wireless Sensor Networks (WSNs) have emerged as a promising tool for gathering real-time information in these hard-to-reach places. However, their deployment is constrained by extreme temperatures, energy limitations, communication instability, and security vulnerabilities. This paper presents a systematic review of WSN applications in desert environments using a PRISMA-based methodology. A total of 70 studies published between 2020 and 2025 were analysed to identify key challenges and state-of-the-art solutions. The review covers energy-efficient routing protocols, solar harvesting techniques, adaptive communication protocols, and lightweight security mechanisms, underscoring reinforcement learning's growing role in self-optimizing network architectures. Findings reveal a significant gap between simulation-based studies and real-world deployments, as existing solutions often address challenges in isolation. Furthermore, the trade-off between energy efficiency and security remains underexplored. To bridge the gap between theory and practical application, the study proposes integrating renewable energy sources with intelligent algorithms to enable reliable WSNs in desert environments
Design and Implementation of a Remotely Monitored Hybrid Renewable Energy System with IoT-Based Relay Control and Real-Time Data Logging
This paper presents a cost-effective smart hybrid renewable energy system (HRES) that integrates solar PV and wind energy systems and supports VRLA battery systems and DC loads. Stand-alone renewable energy systems are subject to intermittence. The design proposed in this paper addresses this issue by integrating both renewable energy systems using a four-channel relay board with an embedded electrical interlock that prevents simultaneous dual-source injection to a PWM charge controller and maintains solar energy priority. The control unit was an Arduino Mega 2560. It read the voltage and current of the PV, wind, and battery systems through the ACS712 Hall effect sensors and B25 voltage dividers, and established a solar-priority interlock. The interlock was improved by remote relay control through the MQTT protocol. The system logged data every 10 seconds in CSV format, displayed information on a 20 × 4 I²C LCD, and sent data wirelessly with the ESP8266 module to a MIT App Inventor mobile application via MQTT. Two test sessions were conducted. Session B (January 16, 2026) was an early validation test. Session C (March 13, 2026) was the main test and was conducted after the sensors were replaced and recalibrated. The combined-source load test (2.83 minutes) was conducted with a mean load power of 25.70 W and a single-sample peak of 26.79 ± 1.5 W (99.2 ± 5.6 % of the rated load). The maximum solar and wind powers were 34.36 W and 10.05 W, respectively. Following the implementation of necessary corrective measures, the relay and measurement consistency for both wind and output was 100%, confirming that the interlock and control logic performed as designed.
Reservoir Characterization of Low-Permeability FACHA Formation in Hakim Field Using ECRIN Well Test Analysis
This study presents the Pressure Transient Analysis (PTA) of two wells in the Hakim field using pressure build-up test data. An Excel-based analysis model was developed to estimate the main reservoir parameters and was validated by comparison with results obtained from ECRIN (Saphir), the industry-standard software for pressure transient analysis. The comparison showed a close agreement between the Excel model and ECRIN results, demonstrating that the developed Excel model provides reliable estimates of reservoir properties. The studied wells, producing from the FACHA Formation, are characterized by low permeability, with permeability values ranging from 3.9 to 10.9 mD. The skin factor ranges from −3.38 to −0.60 for all wells except well A4, which exhibits a positive skin factor of 6.33 due to formation damage. The estimated average reservoir pressure in the Hakim field is approximately 2517 psi.
Dynamic versus Quasi-Steady-State Simulation of Shading Strategies for Cooling Load Reduction: A Case Study of an Educational Building in Tripoli, Libya
Buildings account for roughly 32% of global final energy consumption, and cooling represents the largest share of that demand in hot-arid climates, where educational buildings are particularly energy intensive due to long daily occupancy. This study evaluates five exterior shading configurations, a horizontal overhang, fixed horizontal louvers, louvers inclined at 15° and 30°, and an overhang combined with vertical fins, applied to the southern facade of a two-story, 1600 m² educational building at the University of Tripoli, Libya. The building was modeled in HAP, which applies a quasi-steady-state method, and in EnergyPlus (with SketchUp/OpenStudio), which applies a dynamic transient heat-balance method. A base case without shading was simulated in both tools before each shading system was applied in EnergyPlus. For the July design day, EnergyPlus predicted a peak cooling demand of 181.9 kW against 165 kW from HAP, 10.2% higher; for June, EnergyPlus predicted a total cooling consumption of 20,452 kWh against 38,268 kWh from HAP, 46.56% lower. For January, EnergyPlus predicted a peak heating load of 11.77 kW against 10.5 kW from HAP (12.1% higher) and a heating consumption of 10.56 kWh against 10,189 kWh from HAP (99.9% lower), reflecting HAP's tendency to average loads rather than resolve hourly transients. Among the shading systems, the 30° inclined louvers gave the largest annual cooling reduction (10.27%, from 136,886 to 122,833 kWh) and the largest peak demand reduction (1.51%), followed by 15° louvers (9.81%), horizontal louvers (9.10%), overhang with fins (7.46%), and the overhang alone (5.25%). Adopting this best performing system is estimated to avoid about 14.57 t CO2 per year, based on a national grid emission factor of 1.037 kg CO2/kWh. All five systems increased annual heating consumption relative to the 147 kWh base case, up to 542 kWh (269%) for the 30° louvers, since the geometry that blocks summer solar gain also blocks winter solar gain. Shading selection should balance cooling savings against this heating penalty, and dynamic simulation should be preferred over quasi-steady-state tools when peak and part-load performance matter.
Wavelet-Enhanced DEMON Processing and Deep Learning for Passive SONAR Target Recognition
This paper presents an integrated underwater Automatic Target Recognition (ATR) framework that synergizes advanced signal processing with deep learning. To eliminate non-stationary ambient ocean noise and isolate low-frequency modulations, the raw acoustic pressure signals undergo pre-denoising via Discrete Wavelet Transform (DWT with db4 mother wavelet and soft-thresholding), followed by Detection of Envelope Modulation on Noise (DEMON) analysis to yield 128-bin spectral feature vectors. Classification is performed using a dedicated 7-layer 1D Convolutional Neural Network (1D-CNN) architecture terminating in a Softmax output layer. Acoustic simulations in MATLAB R2024b generated a balanced dataset of 1,200 audio samples (200 samples per class across six distinct targets: fast boat, rubber zodiac, human diver, giant turtle, dolphin, and mini submarine) propagated through a 20–40 path shallow-water multipath channel ( ). Experimental evaluations demonstrate an overall classification accuracy of 94.93% and a macro-averaged F1-score of 0.9505. Coupled with an average inference latency of 11.40 ms per target and Euclidean distance-based thresholding for out-of-distribution anomaly detection, the proposed system proves computationally efficient and well-suited for real-time passive underwater surveillance.
Antimicrobial Activity and Synergistic Effects of Libyan Extracts from Artemisia Herba-alba, Thymus Vulgaris, and Zingiber Officinale Against ATCC Strains of Bacillus Subtilis and Escherichia Coli
The increasing prevalence of antibiotic-resistant bacteria has intensified interest in medicinal plants as a promising source of natural antimicrobial agents. This study aimed to evaluate the antibacterial activity of ethanol, methanol, and aqueous extracts of Artemisia herba-alba, Thymus vulgaris, and Zingiber officinale against Bacillus subtilis and Escherichia coli. Antibacterial activity was assessed using the agar well diffusion, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and checkerboard assays to determine synergistic interactions with Streptomycin. In addition, phytochemical screening and high-performance liquid chromatography (HPLC) analyses were performed. The results demonstrated that Gram-positive bacteria were more susceptible to the plant extracts than Gram-negative bacteria. The ethanolic extract of A. herba-alba showed the strongest antibacterial activity, producing the largest inhibition zone (19.5 ± 0.6 mm) against B. subtilis and the lowest MIC (62.5 mg/mL). The strongest synergistic interaction with Streptomycin was observed for A. herba-alba (FICI = 0.41). HPLC analysis identified caffeic acid as the major phenolic compound (19.3 mg/g), together with other bioactive constituents, including thymol, carvacrol, and gingerol, which may contribute to the observed antibacterial activity. These findings highlight the potential of these plant extracts as natural antimicrobial agents and support further investigations into their pharmaceutical applications.
Bidirectional Cross-Dataset Generalization and Label-Efficient Adaptation for 5G Network Intrusion Detection
Machine-learning-based intrusion detection in 5G networks is often assessed using a single dataset, where strong test results may indicate regularities tied to that dataset rather than attack behavior that generalizes across settings. This study examines bidirectional cross-dataset generalization between 5G-NIDD and NetsLab-5GORAN-IDD through conservative semantic matching of denial-of-service, flooding, and scanning attacks. The assessment uses five-seed zero-shot transfer, a multivariate dataset-fingerprint audit, unsupervised correlation alignment, few-shot target adaptation, and class-blind target-label selection while varying attack prevalences in a controlled manner at 1%, 5%, and 10%. Zero-shot XGBoost performance was highly dependent on both the specific task and the direction of transfer. For matched aggregate tasks, balanced accuracy was 0.518 and 0.505 for application-layer denial-of-service, 0.807 and 0.500 for network/transport flooding, and 0.834 and 0.558 for scanning when transferring from 5G-NIDD to NetsLab and in the reverse direction, respectively. A dataset-source classifier reached balanced accuracy of 0.993 when using all 15 harmonized numerical features and maintained 0.993 after source-predictiveness-aware Top-6 filtering, indicating that dataset identity persisted across multivariate feature interactions. CORAL did not eliminate this fingerprint, whereas using limited target labels produced much larger improvements; in the exploratory adaptation experiment, the mean best gain with no more than 5% labeled target data was 0.346 balanced-accuracy points. Under deployment-like prevalence pressure, Random or Diversity-based querying provided the best overall strategy in 13 of 15 prevalence-budget conditions, while source-model uncertainty sampling was unstable. Overall, these findings indicate that realistic 5G intrusion detection depends on explicit cross-dataset evaluation and limited, target-specific supervision rather than on performance from a single dataset, feature filtering, or straightforward distribution alignment.
Seismic Geochemistry: A Sustainable Approach to Hydrocarbon Exploration in Libya
The growing need for sustainable resource management and environmentally responsible exploration has highlighted the importance of integrating emerging geoscientific approaches into national development strategies. Although Libya possesses significant hydrocarbon resources and diverse geological settings, the application of seismic geochemistry remains limited despite its considerable potential for supporting both energy exploration and environmental management. This study explores the role of seismic geochemistry as an innovative tool for sustainable development in Libya. The proposed framework emphasizes the integration of geochemical indicators with seismic surveys, geological information, well data, hydrogeochemical observations, and environmental monitoring to improve subsurface characterization and resource assessment. Particular attention is given to the identification of geochemical anomalies associated with tectonic activity, hydrocarbon migration, groundwater systems, and environmental hazards. The study demonstrates that integrating seismic geochemistry within multidisciplinary exploration workflows can improve hydrocarbon prospecting, reduce exploration uncertainty, enhance groundwater assessment, strengthen environmental impact evaluation, and support evidence-based resource management. Furthermore, the proposed approach contributes to minimizing environmental risks while improving exploration efficiency through advanced geochemical interpretation. The findings highlight the potential of seismic geochemistry as a strategic decision-support tool capable of promoting sustainable development in Libya by integrating scientific innovation, environmental protection, digital geological analysis, and responsible management of natural resources. The proposed framework further supports sustainable development in Libya's resource-rich regions by enhancing environmentally responsible hydrocarbon exploration, strengthening evidence-based resource governance, and promoting long-term economic and environmental sustainability.
Linear Detection in 28 GHz Massive MIMO Uplink Systems
Massive Multiple-Input Multiple-Output (Massive MIMO) systems represent a cornerstone for meeting the high-performance demands of 5G and beyond networks. This paper presents a comprehensive simulation framework that effectively couples electromagnetic physical characteristics with signal processing algorithms in the uplink transmission. A 16x16 planar antenna array comprising 256 elements was designed to operate at the 28 GHz millimeter-wave band, achieving a high array gain of 32.27 dB alongside precise beamforming capabilities. To evaluate system performance under realistic operating conditions, the spectral efficiency and Bit Error Rate (BER) of three prominent linear detectors—Maximum Ratio Combining (MRC), Zero Forcing (ZF), and Minimum Mean Square Error (MMSE)—were analyzed in the presence of noise and imperfect Channel State Information (CSI). MATLAB numerical simulations demonstrate that the MMSE detector provides the optimal trade-off between performance and computational complexity, achieving a BER of 10^ {-4} at an SNR of 15{dB}. Conversely, the ZF detector exhibited severe performance degradation due to noise enhancement under channel estimation error conditions (sigma_e^2 = 0.01). These findings offer practical insights for selecting scalable detector architectures in future wireless networks.
Structural Performance of Castellated Steel Beams with Flange-Connected Ring Stiffeners and Inverted Stiffener Geometry
This paper aims to study the behavior of castellated beams with large openings (D/H = 0.7) with a constant offset of 10 mm. Different configurations, including ring stiffeners connected to the flanges and geometrically inverted ring stiffeners, were investigated. Nonlinear finite element models in ABAQUS were used to compare circular and hexagonal openings. The hexagonal openings with flange-connected showed an increase in the ultimate load by up to 6.12% and improved the ductility factor from 5.65 to 6.33. Connecting the ring stiffeners of the circular openings to the flanges slightly reduced the ultimate load by up to 1.67%, while the ductility factor decreased from 4.25 to 3.39. The inverted geometrical cases led to lower performance, especially when the hexagonal openings were strengthened with a circular stiffener, with a decrease of 11.36%. Ring stiffeners connected to the flanges of castellated beams with large hexagonal openings can be used to control the web post buckling.
Robust Biomechanical Classification of Humans, Gorillas, and Robots under Urban Multipath Fading using Micro-Doppler Radar
This paper presents a physically grounded framework for classifying humans, gorillas, and robots using a 35 GHz Ka-band continuous-wave radar under realistic urban multipath conditions. Urban deployments face severe signal distortion due to multipath fading; Micro-Doppler radar is a pivotal tool for non-contact biomechanical classification. A total of twenty features are obtained from both biomechanics and spectroscopy and selected using Neighbourhood Component Analysis (NCA). Stacking ensemble learning that employs Random Forest, Support Vector Machine, K-Nearest Neighbours, and MLP, as well as linear support vector machine for meta-classifier is evaluated in five-fold stratified cross-validation using 400 original samples, augmented to 1200 using MATLAB (R2023b) . The system achieves a state-of-the-art accuracy of 90.92% ± 2.03% (95% CI: [89.14%, 92.69%]) with F1-score of 90.88% ± 2.08% under harsh urban conditions (SNR = 15 dB, K-factor=7 dB). The error analysis reveals that the use of multipath-aware augmentation and supervised feature selection leads to a significant reduction of the spectral smearing phenomenon, resulting in reduced errors for classifying human from robot at an impressive 68%. The gorilla is the class that shows the highest inter-class separability, with 95.0% accuracy. The robustness analysis demonstrates consistent behaviour across various signal-to-noise ratio and K-factor combinations due to the stacking ensemble technique that eliminates any distribution shift within the feature space. Ka- and radar detection enhances the frequency resolution of the Micro-Doppler features, improving the inter-class separability while accounting for high-order scattering phenomena. This work provides a comprehensive baseline for Micro-Doppler human-robot classification in an urban environment and it is noted that this study relies on simulation-based validation; thus, real-world Ka-band radar data is required for final empirical verification.
Urban multipath fading; Stacking ensemble
Geochemical Approaches to Combating Desertification and Enhancing Sustainable Development in Libya
One of Libya's biggest environmental problems is desertification, which is caused by the country's arid and semi-arid climate, low rainfall, high temperatures and evaporation, drought, groundwater depletion, soil salinization, wind erosion, vegetation degradation, and unsustainable land-use practices. The potential of geochemical methods for comprehending, tracking, and preventing desertification as well as promoting sustainable development in Libya is assessed in this study. In order to examine climatic and environmental changes from 2000 to 2025 and their implications for future desertification, the study combines geochemical examination of soil and groundwater with satellite remote sensing and spatial analysis. Temperature, rainfall, and vegetation-cover variations were investigated using MODIS Terra land surface temperature (LST), NASA GPM IMERG precipitation, and Landsat data. With average July LST rising from roughly 29.6°C in 2000 to 34.3°C in 2025 and yearly rainfall falling from 98.6 to 55.2 mm, the results show a significant warming trend. Additionally, there is a general decrease in vegetation cover, especially in inland agricultural areas, oasis systems, and portions of mountainous and coastal regions. By 2050, almost 80% of Libya's geographical area might undergo high to intense desertification, according to a business-as-usual forecast. For evaluating soil fertility, salinity, nutrient availability, pollutant movement, groundwater quality, water–rock interactions, and seawater intrusion, geochemical techniques offer useful instruments. As a foundation for sustainable land and water management, the study highlights the combination of geochemistry, remote sensing, GIS, sophisticated analytical technologies, isotopic techniques, and data science.
Study of Natural Marble Specifications in Southern Libya
This study evaluates the chemical, physical, and mechanical specifications of natural ornamental stones from five quarry sites in the Wadi Alshatti / Zella region of southern Libya: Tarouth, Mahruqa Right, Mahruqa Left, Zella, and Al-Ghaylaniya, all locally marketed as "marble." Samples were characterized by X-ray Fluorescence (XRF) chemical analysis, bulk density and water absorption (ASTM C97), uniaxial compressive strength (ASTM C170), and sulfuric-acid (H2SO4) resistance, with three replicate specimens per site. Results show that Mahruqa Left exhibits the highest compressive strength (87.45 ± 0.77 MPa) and CaO content (87.8%), with low water absorption (0.31%) and a density of 2.68 g/cm3, consistent with a high-purity calcitic marble. Tarouth (CaO = 79.7%; 64.93 MPa) and Mahruqa Right (CaO = 75.9%; 64.00 MPa, currently inactive) classify as dolomitic and calcite-dolomite marbles, respectively. Chemical analysis reveals that the Zella stone is not true marble: its exceptionally high sulfate content (SO3 = 31.91%) identifies it as a calcite-sulfate onyx of evaporitic/hydrothermal origin rather than a metamorphic carbonate rock. Similarly, Al-Ghaylaniya, with MgO = 19.60%, the highest water absorption (8.78%) and the lowest compressive strength (34.30 MPa), is geologically a dolomitic travertine. These findings indicate that two of the five commercially traded "marble" sources are mineralogically distinct stone types, with direct implications for correct classification, pricing, and end-use selection in the Libyan dimension-stone industry.
The Impact of Fuel Type on the Economic, Environmental, and Energy Performance of Power Generation Plants: A Case Study of the Ubari Gas Power Plant-Libya
This study aims to analyze the effect of fuel type on the thermal, environmental, and economic performance of power generation plants. The Brighton cycle model, with ideal gas and stable operation assumptions, was used to evaluate turbine performance using crude oil, natural gas, diesel, and heavy fuel oil. The results showed that natural gas achieved the highest thermal efficiency (up to 35.7%), the lowest CO2 emissions, and the lowest energy production cost compared to the other fuel types. Crude oil and heavy fuel oil, on the other hand, exhibited the lowest thermal performance, highest emissions, and highest energy production cost. The study also revealed that specific fuel consumption increases with decreasing thermal efficiency. These findings highlight the importance of fuel type selection in improving plant efficiency and reducing environmental impact, making natural gas the optimal choice for gas turbine operation.
Assessing the Effectiveness of Ondansetron and Dexamethasone in Reducing Nausea and Vomiting during Caesarean Section under Spinal Anaesthesia: A Systematic Descriptive Review
Background: Elective cesarean sections are commonly treated with spinal anesthesia, which is the most commonly used analgesic method. Nausea and vomiting following cesarean section with spinal anesthesia are frequently reported concerns of patients. The purpose of this study was to assess the effectiveness of medications in reducing nausea and vomiting in women who have undergone a cesarean section and spinal anesthesia. Methods: To conduct a systematic review, we searched the PubMed and Google Scholar databases for keywords such as nausea, vomiting, cesarean section, spinal anesthesia, ondansetron, and dexamethasone. The search for articles took place from 2014 to 2022. Results: Six studies were included, which consisted of five randomized controlled trials (RCTs) and one non-randomized clinical trial (NRCT). Similar drugs were used in these studies with different dosages to prevent nausea and vomiting following a cesarean section. The use of intravenous dexamethasone, intravenous ondansteron, and their combination was a part of the interventions. Conclusion: Dexamethasone's delayed effect on nausea and vomiting is not a concern, but given the findings, it could be a safe alternative to Ondansetron for postoperative nausea and vomiting prophylaxis. The preferred first-line agents seem to be Ondansetron, which is a 5-HT3 receptor antagonist. Using Ondansetron and dexamethasone together has been shown to help decrease nausea and vomiting during and after a cesarean delivery using spinal anesthesia.
Surra Infection in Imported Camels: The First Detection in Southern Libya
Camel trypanosomiasis is considered one of the most important veterinary health problems worldwide. A parasitological investigation was conducted for the diagnosis of trypanosomiasis using Giemsa-stained thin blood smears. A total of 130 blood samples were examined, of which 115 samples (88.4%) were negative, while 15 samples (11.5%) were positive for Trypanosoma spp. This study represents the first documented record of camel trypanosomiasis in southern Libya. An epidemiological survey was carried out in March 2023 to estimate the prevalence of Trypanosoma spp. infection among imported dromedary camels (Camelus dromedarius) in Sebha City, southern Libya. To date, no published studies have reported trypanosomosis in this region. The results showed a higher prevalence of infection in male camels (6.9%) compared with females (4.6%). In addition, the infection rate was greater among male and female camels older than 10 years than among younger age groups. Microscopic examination of thin blood smears confirmed the presence of Trypanosoma infections regardless of age or sex. The findings of the present study indicate that trypanosomiasis is common in the investigated area. Considering these results, the potential health risks and associated diseases affecting camels warrant further attention and implementation of appropriate control measures.
Feature-Selected Machine Learning for 5G Intrusion Detection: Leakage-Aware Evaluation and Sensitivity to Sequence Metadata in 5G-NIDD
Fifth-generation networks present a multifaceted and software-centric attack surface, thereby necessitating a systematic approach to reproducible intrusion-detection evaluation through traffic data gathered from authentic 5G environments. This investigation examines binary intrusion detection utilizing the comprehensive 5G-NIDD Combined.csv dataset acquired from its official IEEE DataPort distribution, adhering to a standardized leakage-aware methodology. The original dataset comprised 1,215,890 records and 52 columns. Following the elimination of 21 exact duplicate entries, 1,215,869 observations were retained. A stratified 70:30 partition was executed at the record level prior to preprocessing, with all data-dependent procedures being exclusively calibrated on the pertinent training dataset. The application of zero-variance filtering, Pearson-correlation reduction, and ANOVA F-test ranking condensed the 46 usable predictors into a consistent Top-10 representation. Random Forest, XGBoost, and a scalable random Fourier feature support-vector machine (RFF-SVM) approximation of an RBF-kernel classifier were assessed employing an untouched holdout partition in conjunction with five-fold stratified cross-validation, incorporating fold-specific preprocessing and feature selection. XGBoost and Random Forest attained Top-10 holdout accuracies of 99.9748% and 99.9745%, respectively, with no statistically significant disparity between their paired errors subsequent to Holm correction. The full, correlation-reduced, and Top-10 representations yielded virtually identical outcomes for both ensemble models, suggesting that the compact feature set maintained rather than enhanced random-split performance. Nevertheless, Seq and Offset emerged as the two highest-ranked predictors. The exclusion of these predictors resulted in a decline in holdout accuracy to 71.18% forXGBoost, 71.16% for Random Forest, and 73.09% for RFF-SVM, with a similar trend noted during cross-validation. These results illustrate that nearly flawless record-level outcomes are heavily contingent upon acquisition-order metadata and should not be construed as indicative of cross-session or cross-environment generalization. Prior to operational implementation, session-aware, base-station-aware, and external validation are imperative.
Association of Vitamin D Levels with Asthma Control among Pediatric Patients in Wadi Al-Shatti, Libya
Background: Asthma is the most common chronic inflammatory respiratory diseas in children. It has significant impact on morbidity and reduced quality of life worldwide. While recent research has highlighted the potential of vitamin D as an immunomodulator in asthma pathophysiology and control, the evidence remains controversial. Furthermore, data regarding this relationship in North African populations, particularly in Libya, are notably limited. Objective: This study aimed to investigate the relationship between vitamin D status and asthma control among asthmatic children and adolescents in Wadi Al-Shatti region, southern Libya. It further sought to assess serum 25-hydroxyvitamin D (25(OH)D) levels and their associated factors, as well as exploring the correlation of the targeted cases with immunological responses and key biochemical indicators. Methods: A total of 50 children aged 1–15 years were enrolled in this observational study. Participants were identified and recruited from Brack General Hospital and Qaqam General Hospital. Data collection was performed in two intervals: January–March 2023 and December 2023–March 2024. Asthma control was assessed according to the GINA guidelines and the Childhood Asthma Control Test (C-ACT). Laboratory investigations included serum 25-hydroxyvitamin D, total immunoglobulin E, calcium, phosphorus, alkaline phosphatase, and complete blood count. Statistical significance was defined as p < 0.05 using SPSS version 26. Results: Among the 50 participating children, the majority of participants were males (64%), compared to female (36%), of whom 80% presented with uncontrolled asthma. Vitamin D deficiency was highly prevalent, with the majority of targeted individuals exhibiting serum levels below 20 ng/ml. A significant correlation was found between vitamin D levels and asthma control, with lower levels more frequently identified in children with uncontrolled disease. Furthermore, significant associations were identified between vitamin D levels and serum IgE concentrations, as well as serum phosphorus levels. Additionally, red blood cell counts showed a significant association with vitamin D status, whereas calcium and alkaline phosphatase levels remained non-significant. Conclusion: Children with poor asthma control have a high incidence of vitamin D deficiency. These findings suggest that vitamin D status may represent an important modifiable factor influencing asthma outcomes in paediatric populations.
Study of Iron Deficiency Anemia Among Lactating Women in Mahrouka Ashatti Region- Southern Libya
Iron deficiency anemia in women is one of the most common nutritional problems, that is more prevalent in adolescents, pregnant and lactating (breastfeeding) women. To determine the prevalence of anemia, iron deficiency anemia and some of its causes in a group of lactating women who attending at health care centers in Mahrouka Al shatti region- southern Libya, EDTA blood samples were collected from 57 lactating women aged 19- 42 years in 15 Aug. to 20 Sep. 2018, for complete blood count and serum iron, Total Iron Binding Capacity for anemic women(Hb< 12gr/ dl). Thirty four lactating women (59.65%) were anemic, 26 (76.47%) had mild anemia, 7(20.59%) had moderate anemia and 1(2.94%) had severe anemia and 52.94%(18) were iron deficiency anemia. There were differences showed in hemoglobin values, with no correlations between Hb and women age, and between Hb and period of lactation. IDA and anemia were more prevalent in the lactating women and its relation to menstruation distributions, iron loss, breastfeeding and stress.
Comparison of Water Sorption and Solubility of CAD/CAM Milled PMMA, 3D-Printed PMMA, and 3D-Printed Resin Dental Polymers
The objective of this study was to evaluate and compare the water sorption and solubility of three dental restorative polymers—CAD/CAM milled PMMA, 3D-printed PMMA, and 3D-printed resin—after aging for different time intervals. For this purpose, thirty specimens (n = 10/group) were fabricated from CAD/CAM milled PMMA (PMMA DISC), 3D-printed PMMA (Temporary Crown), and 3D-printed resin (SDC Resin). Specimens were dried in a ventilated oven at 45 °C for 24 hours, and then immersed in purified water at 23 ±1°C. The masses were measured after 7, 14, and 30 days. Water sorption (Wsp) and solubility (Wsl) were calculated in µg/mm³. Assumptions of normality, variance homogeneity, and sphericity were tested. Sorption was analyzed via two-way repeated measures ANOVA with Games-Howell post-hoc; solubility, which violated normality, via Kruskal-Wallis with Dunn's post-hoc (α = 0.05). The achieved results showed that water sorption increased significantly with immersion time in all materials (p < 0.001). 3D-Resin demonstrated the highest sorption values, followed by 3D-PMMA, while Milled-PMMA exhibited the lowest values at all time intervals. At 30 days, mean sorption values were 16.77 ± 0.58 µg/mm³ (Milled-PMMA), 34.18 ± 3.68 µg/mm³ (3D-PMMA), and 47.68 ± 2.07 µg/mm³ (3D-Resin). Water solubility differed significantly among the tested materials (p < 0.001), with 3D-PMMA showing the highest solubility (5.39 ± 4.10 µg/mm³), followed by 3D-Resin (3.12 ± 1.63 µg/mm³), while Milled-PMMA exhibited negative values (−0.40 ± 0.26 µg/mm³). Consequently, it was concluded that material composition and immersion duration have a significant influence on water sorption and solubility of dental polymers. CAD/CAM milled PMMA exhibited higher hydrolytic stability, whereas 3D-printed resins demonstrated higher sorption and solubility, which makes them more susceptible to hydrolytic degradation. Clinically the materials selection for digitally fabricated restorations is critical, as the increased water sorption and solubility in 3D-printed resins may affect long-term clinical performance.
A Hybrid Steady-State Mathematical Model for a Brine-Recycle Multistage Flash Desalination System
A steady-state mathematical model for a brine-recycle multistage flash (MSF-BR) desalination system is developed for performance prediction and analysis. The proposed formulation combines the computational simplicity of section-wise steady-state models with selected rigorous thermodynamic features while avoiding the complexity of detailed stage-by stage formulations. The governing model consists of coupled mass, salt, and energy balance equations combined with heat-transfer relations for the recovery section, rejection section, brine heater, and mixing units. The resulting nonlinear algebraic equations are linearized using a modified tridiagonal model (TDM)-based approach and solved iteratively within a FORTRAN computational framework. The model is validated using published benchmark data for a 5 MGD brine-recycle MSF desalination plant reported by Rosso et al. Predicted temperatures, flow rates, salinities, heat-transfer coefficients, distillate production, and gain output ratio (GOR) show good agreement with the reference results, with deviations generally within acceptable engineering limits. The model also reproduces the effects of steam and seawater temperatures on plant performance. While deviations in distillate production and gain output ratio increase at elevated steam temperatures, the agreement remains satisfactory within the practical operating range of MSF desalination plants. The results demonstrate that the proposed model provides reliable steady-state predictions with moderate computational effort. Owing to its balance between physical realism and numerical simplicity, the model represents a practical tool for simulation, parametric analysis, and preliminary optimization of large-scale MSF desalination systems.