Ischemic brain stroke is the most prevalent type of stroke caused by the occlusion of blood vessels via thrombi or blockages and is the second most common cause of mortality globally, after ischemic heart disease. To improve patient outcomes, ischemic stroke must be diagnosed timely and precise. This paper introduces a novel approach toward ischemic stroke detection from computed tomography (CT) images by integrating deep learning and optimization techniques for feature extraction and selection. The VGG16 model is utilized to extract high-dimensional spatially rich features that efficaciously capture the intricate texture and spatial patterns within the CT scans. To optimize these features, a genetic algorithm (GA) is leveraged to select the most discriminative subset and reduce redundancy. The new method was developed and evaluated on a unique, first-hand dataset gathered from a specialized private hospital in Palestine. The findings show that the suggested combined technique VGG16-GA highly enhances the performance of all classifiers. Notably, the VGG16-GA-XGB model attained superior outcomes, with an accuracy of 98.89%, precision of 98.85%, recall of 98.93%, and an F1-score of 98.92%.

Resonance occurs when the operating frequency of a system aligns with its natural frequency, resulting in amplified vibration amplitudes. To prevent potential damage and ensure optimal performance of a compressor's base frame at Souq Al-Khamis Cement Factory, researchers found the resonance in has been occurred when both the natural frequencies and rotating frequency were overlapped. Resonant Vibration in the base frames arises when the rotating vibration frequency aligns with the frame’s natural modes that leads to structural instability, fault unplanned shutdowns and production losses. This study analyzes resonant vibration in a cement factory compressor base frame and proposes a redesign using finite element methods to mitigate this issue. Four distinct modifications were made to the base frame on its shape, weight and boundary conditions: the first introduces fixed points to enhance rigidity, the second adds supports for increased stability, the third incorporates elements to improve durability, and the fourth enhances the thickness of the compressor. The results indicate that the redesigned configuration most effectively mitigates resonance and improves the system's natural frequency response.

The progressive deterioration of flexible pavements, driven by increasing traffic loads, environmental influences, and material aging, necessitates the implementation of effective structural reclamation strategies to restore functional performance and extend service life. This study focuses on a critical segment of the Libyan coastal road network, specifically the 27.5 km part from Tripoli Street Bridge to Al-Krarim Gate. An analytical and quantitative investigation is undertaken to evaluate the structural condition and rehabilitation potential of the existing flexible pavement system, with particular attention to distress mechanisms, material degradation, and the effectiveness of various rehabilitation techniques. The assessment integrates field investigations, laboratory testing, based on AASHTO 1993, and to evaluate pavement distress, base soil strength, and asphalt concrete layers performance; Pavement Condition Index (PCI) values and core sample analyses are employed to determine the extent of structural failure. Visual distress surveys supplement the data to provide a comprehensive understanding of surface and sub-surface conditions. Analytical modeling, based on layered elastic theory is used to simulate pavement response under rehabilitated conditions and forecast long-term performance under loading. The study examines several rehabilitation methods, including full-depth reclamation (FDR), cold in-place recycling (CIR), and mechanical stabilization using cementitious additives. Each method is evaluated based on structural capacity enhancement, cost-efficiency, and service life extension. Results demonstrate that the selection of reclamation techniques tailored to subgrade conditions and traffic loads significantly improves structural performance and minimizes maintenance needs. The study concludes that full-depth repaving offers the most sustainable and economically viable solution for restoring the targeted roadway section.

Countries located in temperate, hot and arid climates, such as Libya, face the critical need to cool houses whose internal temperatures rise due to these climatic conditions. This can be achieved by employing proper insulation techniques to prevent heat gain from solar radiation (insolation). This paper addresses the impact of not implementing thermal insulation for the roof of a building, in contrast to other external parts of the structure. The temperature distribution in a single-story building was studied using finite element analysis (FEA), along with how the building absorbs heat from its surroundings during a sunny day. The thermal analysis was conducted on a 3D concrete building with walls made of concrete masonry blocks, a floor height of 3.20 meters, and a total area of 40 square meters, using ANSYS 2020 R2 software. The building model includes thermal insulation for the external envelope, but the roof and openings remain uninsulated (as is often the case with home insulation practices in Libya). The finite element method is widely used due to its high effectiveness in simulation and achieving accurate results. The analysis results demonstrated the heat distribution gained from insolation, as well as variations in the rates of heat transfer from the building's exterior to its interior. The findings showed that neglecting the thermal insulation of the roof and window openings leads to an approximate 70% increase in the building's internal temperature. Furthermore, the results clearly indicated that insulating the building's walls alone is insufficient to prevent overheating. This provides a sufficient understanding of the prioritization required in applying insulation layers for buildings located in hot climates.

Diabetic cardiac autonomic neuropathy (DCAN) is a significant condition that affects cardiovascular health worldwide and is associated with increased morbidity and mortality rates. Therefore, early detection and management of DCAN are crucial for reducing the risk of cardiovascular disease among individuals with T2DM. Identifying this disorder can enhance patient outcomes and quality of life by minimizing the chances of serious complications. This cross-sectional study aims to identify diabetic individuals with DCAN and to investigate its relationship with various risk factors, including hyperglycemia, the duration of diabetes, the presence of peripheral somatic neuropathy, and diabetic microvascular complications. The study included 261 patients with T2DM, comprising 61.5% females and 38.5% males. Participants underwent cardiovascular testing and clinical evaluations to identify cases of cardiac autonomic neuropathy. Out of the 261 randomly selected patients, 82 were diagnosed with DCAN, resulting in a prevalence rate of 31.4%. The average age for female patients was 57.5 ± 0.7 years, while for male patients, it was 56.3 ± 1.2 years from the total recruited patients. In addition, there is a strong association between DCAN and clinical and biochemical parameters such as lipid profile, duration of diabetes, poor diabetic control, and presence of microalbuminuria in patients with DCAN and above 60 years old, compared to younger patients. The study highlighted a strong association between DCAN and factors such as poor glycemic control, prolonged diabetes duration, and the presence of chronic microvascular complications, including neuropathy, retinopathy, and nephropathy. These findings emphasize the importance of raising awareness and proactively assessing Libyan patients who are at risk for cardiovascular autonomic neuropathy. This is crucial to reduce the likelihood of recurrent acute cardiac complications, especially in patients undergoing emergency surgery without a prior diagnosis. It is vital to recognize this risk.

Keywords. Type 2 Diabetes Mellitus, Pulse Rate, Metabolic Syndrome, Blood Pressure.

Abstract— Arabic language is characterized by its rich morphological structure, presenting unique challenges in Nat- ural Language Processing (NLP). The categorization of Arabic plurals is the subject of this study, which uses a trans- former-based model—more precisely, the pre-trained Arabic BERT architecture—and has never been studied previously. Given the complexities of Arabic language, particularly in pluralization which includes sound masculine, sound feminine, and irregular (broken) plurals, the research aims to enhance NLP capabilities in this area. By utilizing a dataset of 7,400 instances classified into four distinct categories, the study demonstrates the effectiveness of transfer learning in achieving high classification accuracy, with results indicating an accuracy of 97% across both validation and testing sets. Addition- ally, the model achieves high precision, recall, and F1-score metrics. A confusion matrix provides insights into classifica- tion performance, highlighting areas of misclassification. The findings underscore the potential of transformer models in overcoming the linguistic challenges posed by Arabic plural forms.