The examination of the flow instability is crucial in nuclear reactors to prevent the occurrence of flow excursion during a postulated accident. Instabilities can reduce the safety margins against these critical heat flux phenomena, where the fuel cladding surface temperature increases dramatically. In this paper, flow instability is investigated at the reactor of Tajoura Nuclear Research Centre with low enriched uranium (LEU) core, the recent reactor’s fuel assembly type is IRT-4M. The flow instability is assessed at the hottest cell of the reactor by implementing PLTEMP/ANL code.

The safety margin to flow instability is evaluated at the maximum power of the reactor (9.7 MW) by running the code at various core flow rates to find the point where FIR equals 1. The inlet coolant temperature is 45°C, the core pressure drop is 0.066 MPa, and the inlet pressure is 0.179 MPa. At the beginning, the calculations have been obtained at the flow rate of 8.1 kg/s. The results show that the maximum cladding surface temperature is 96.971°C which is less than the permissible value (102°C), the minimum FIR, ONBR, and DNBR equals to 2.034, 1.559, and 4.147, respectively. By reducing the core flow rate at the maximum power level, it is determined that FIR equals to 1.0 at the flow rate of 3.976 kg/s. Which means that the safety margin value to flow instability is 2.038. It is concluded from the safety margin value to flow instability that it is in agreement with the thermal-hydraulic safety requirements of TNRC’s reactor. 

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

Gas flaring is a major source of carbon dioxide (CO2) emissions in oil-producing countries and remains an acute environmental challenge in Libya. The country’s instability has repeatedly disrupted oil and gas operations, leading to highly volatile flaring behavior and complicating long-term mitigation efforts. This study applies time-series forecasting to model and project per-capita CO2 emissions from gas flaring in Libya using two forecasting frameworks: Exponential Smoothing with additive trend (ETS) and an Autoregressive Integrated Moving Average (ARIMA) model, based on annual data from 1967 to 2024. Comparative model evaluation shows that ARIMA outperforms ETS, achieving a root mean squared error of 0.373 and a mean absolute error of 0.327, compared to 0.406 and 0.367 for ETS. Baseline ARIMA forecasts project emissions of 1.27tCO2 capita −1 by 2030, remaining well above the near-zero flaring threshold of approximately approximately 0.05tCO2 capita −1. Scenario analysis shows that attaining this target would require an average annual reduction of approximately 44% starting from 2024, far exceeding historical reduction rates observed during 1967-2024. These results indicate that, under current trends, Libya is unlikely to achieve zero routine flaring by 2030 without substantial structural, infrastructural, and regulatory intervention.

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.

Planetary gear systems in industrial equipment, characterized by highly integrated structures, non-stationary operating conditions, and heavy-load characteristics, result in significant attenuation of fault-related vibration features, severely limiting the effectiveness of fault diagnosis. Traditional studies often use periodic modulation terms, such as the Hanning window function, to approximate the spatiotemporal distribution characteristics of meshing forces. Although these methods can effectively extract global vibration features of healthy gears (such as meshing frequency and its harmonics), they have significant limitations and are unable to analyze the nonlinear diffusion process of fault impacts through complex paths, resulting in distortion in the phase delay and amplitude attenuation patterns of fault impacts. To address these issues, this study investigates the vibration transmission characteristics of a planetary gearbox through rigid-flexible coupling simulation analysis, focusing on the transmission delay effects of impact responses to the vibration sensors at different housing locations. The study qualitatively clarifies the intrinsic relationship between transmission delay characteristics of the gear-sensor spatial relationship, providing a theoretical foundation for accurate analysis of vibration signals in planetary gear sets. The research highlights the significant spatiotemporal characteristics of the vibration responses when the fault collisions occur at different locations during the rotation and revolution of planet gears.

Rotating machinery is a critical component in mechanical systems, widely used across industrial applications. Due to time-varying speed conditions and complex operating environments, it is highly prone to various failures. Without timely diagnosis and maintenance, such failures can lead to significant performance degradation or catastrophic outcomes. To address the challenges posed by non-stationary operating conditions and vibration signals, researchers have developed diverse fault diagnosis methods, including advanced non-stationary signal processing techniques and data-driven approaches. Among these, generalized demodulation (GD) has demonstrated particular effectiveness in extracting fault-related features from complex signals. This paper provides a comprehensive review of GD-based fault diagnosis methods for rotating machinery. It revisits the fundamental concepts and theoretical basis of GD, analyzes the limitations of traditional approaches, and systematically compares GD with other widely used methods. Furthermore, existing GD-based techniques are categorized into speed-dependent and speed-independent methods based on their reliance on rotational speed, with representative studies and applications discussed. Finally, future research directions and current challenges in GD-based diagnosis are outlined, offering valuable insights for researchers and practitioners in the field.

Abstract— This study examines the current state of conformity assessment (CA) in Libya, with a specific focus on the cement manufacturing sector as a case study. Conformity assessment, encompassing testing, inspection, certification, and accreditation, plays a critical role in ensuring product quality, safety, and market access. Libya faces challenges in implementing effective CA practices, hindering its economic diversification. This research evaluates stakeholder awareness, infrastructure adequacy, legal/institutional frameworks, and alignment with international standards using a mixed-methods approach. A stakeholder survey (N=54) and qualitative analysis of Libyan legal and regulatory documents were employed. Key findings reveal nominal CA awareness (82.69%), yet practical implementation gaps exist due to inadequate infrastructure (18.8% of respondents citing this as an obstacle), weak enforcement (18.8%), and limited technical expertise. The cement sector showed low Quality Management Systems (QMS) adoption (48%) and inconsistent adherence to the Libyan Portland Cement Standard LNS 340:2009. While support for aligning with international standards is strong (average rating 4.04/5), obstacles like lack of awareness (31.1%) and technical expertise (30.2%) impede progress. The study proposes actionable recommendations to strengthen Libya’s CA system, including developing a unified national framework, investing in accredited laboratories, and promoting collaboration.

Keywords— Conformity Assessment, Cement Industry, Libya, Quality Standards, Economic Diversification, Stakeholder Awareness.

Early and precise diagnosis of brain tumors is essential for successful treatment planning and improved patient outcomes. This paper introduces a novel hybrid deep model that incorporates DenseNet121, a convolutional neural network (CNN), and the Swin Transformer, a vision transformer model, by feature-level fusion to classify brain tumors from magnetic resonance imaging (MRI) scans. The suggested method provides a more discriminative and better representation by uniting the global context capability of the Transformer model with the local feature extraction capability of the CNN model. The suggested method was trained and assessed on a publicly available brain MRI dataset of four classes: glioma, meningioma, pituitary tumor, and no tumor. Experimental results indicate that the proposed approach outperforms many baseline models including VGG16, MobileNetV2, and AlexNet with an accuracy of 99.39%, precision of 99.36%, recall of 99.34%, and F1-score of 99.35%. Grad-CAM was utilized to visualize class-discriminative regions in the MRI scans to enhance interpretability, hence validating the model's emphasis on tumor-relevant regions. These outcomes prove the efficacy of coupling Transformer and CNN architectures in obtaining accurate and interpretable brain tumor classification from MRI scans.

 Fuzzy Neural Networks (FNNs) enhance conventional Artificial Neural Networks (ANNs) by incorporating fuzzy membership functions, which enable the handling of uncertainty, ambiguity, and imprecise information. While Fuzzy Backpropagation Neural Networks (FBNNs) improve classification performance across noisy datasets, the effectiveness of fuzzification heavily depends on the proper tuning of membership function parameters—typically optimized manually. This paper presents a novel Artificial Immune System framework for optimizing Fuzzy Backpropagation Neural Networks used in the classification of biological image data. The approach integrates a fuzzy min–max fuzzification layer with a feed-forward backpropagation network and applies an optimization version of an Artificial Immune Network model, derived from opt-aiNet, to tune trapezoidal membership functions. Experimental results confirm that the proposed immune-driven optimization is an effective technique for enhancing FBNN robustness and generalization.