Sana'a University Journal of Applied Sciences and Technology

In Yemen, telecom projects frequently experience schedule delays, which in turn impede the successful execution of strategic initiatives and the effective deployment of new public services. This study aims to investigate the internal factors causing schedule delays in Yemen-Mobile’s transceiver sites projects. A re-structured questionnaire was distributed to employees involved in the implementation of transceiver sits projects in Yemen Mobile. Of the 191 questionnaires distributed, 154 valid responses were collected and used for analysis. The study identified ten major factors that have a significant detrimental influence on project schedules and caused delay. Procurement phase - related issues emerged as the most influential. These factors included a lack of technical equipment, supplier-related delays, lengthy purchasing procedures and unqualified technical equipment suppliers. Furthermore, Delays in the implementation phase also include delays in fiber optic network connection, security-related material and equipment delays to the project site and implementation start barriers. In addition to, the lack of comprehensive procurement planning, ineffective the strategy of addressing challenges of sites acquisition and lack of monitoring and control plans were identified as a critical planning issue. The study provided practical recommendations for improving procurement management, boosting the efficiency of the implementation phase, and addressing planning issues.

This paper investigates the application of Artificial Intelligence (AI) in Self-Organizing Networks (SON) for 5G networks, focusing on coverage enhancement and reduction of Operational Expenditures (OPEX). A conceptual AI-Self Organizing Networks (SON) framework integrated with O RAN architecture is proposed, and an illustrative Python-based simulation is conducted to demonstrate potential trends in coverage probability, energy consumption, and estimated OPEX savings. The simulation results indicate that AI-SON can achieve near-optimal coverage (coverage probability 0.9985) while reducing energy usage and maintenance costs, with an estimated OPEX reduction of 2030% compared to baseline strategies. The study clarifies that the simulation is illustrative and not experimentally validated, providing a foundation for future rigorous evaluations.

Recent advancements in Natural Language Processing (NLP) and Text-to-SQL systems have enabled easier interaction with relational databases. However, most solutions focus on English, leaving Arabic underrepresented. This study addresses that gap by fine-tuning the Llama-3-SQLCoder model to convert Arabic text into correct and executable SQL, enabling non-technical users to work with databases without learning SQL syntax. We enhanced the model using Low-Rank Adaptation (LoRA) and Unsloth, training it on Arabic questions paired with SQL queries from the Northwind database. To support low-resource environments, the model was converted to the GGUF format, reducing computational requirements while preserving performance. Evaluation results showed an execution accuracy of 90.24% and a validity rate of 97.56%, outperforming the zero-shot baseline (44% and 80%). The model also achieved an Exact Match score of 32% and an F1 score of 0.83, compared to 12% and 0.61 for the baseline. These findings demonstrate that LoRA and Unsloth are effective for adapting SQL-specialized models to Arabic. Despite these improvements, the system still struggles with complex nested queries and dialectal variations, indicating areas for future work. Overall, this study contributes to narrowing the gap between Arabic and other languages in Text-to-SQL research and improves database accessibility for non-technical users.

Bacillus species represent one of the most dominant soil bacteria and are capable of producing diverse metabolites with antimicrobial properties. This study aimed to isolate Bacillus spp. from Yemeni soils and evaluate their antimicrobial activity. Thirty soil samples were collected, and the isolates were characterized using phenotypic and biochemical tests. Their antimicrobial potential was assessed against Staphylococcus aureus, Escherichia coli, Pseudomonas sp., Salmonella sp., and Candida albicans using agar disc and well diffusion methods. In agar disc assays, the strongest inhibitory effects were observed against Salmonella sp. (13–32.5 mm), followed by E. coli (13–25.4 mm), C. albicans (10–18.5 mm), and S. aureus (10–15.5 mm). Five isolates exhibited antibacterial activity against Salmonella sp. in well diffusion assay. These results highlight the significant antimicrobial potential of Bacillus isolates derived from Yemeni soils. Further studies are recommended to identify bioactive metabolites and explore their suitability for the development of novel therapeutic agents.

This study presents a comprehensive, high-resolution assessment of the theoretical and practical solar energy potential in the Republic of Yemen using satellite-derived data from the SolarGIS platform, available via the Global Solar Atlas. The analysis incorporates key solar radiation components including Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DIF), Global Horizontal Irradiance (GHI), and Global Tilted Irradiance at optimal tilt (GTI), along with auxiliary factors such as optimal tilt angle, air temperature, and elevation above sea level. GHI values across Yemen range between 5.46 and 6.88 kWh/m². Approximately 7.62% of the country's area exhibits the highest theoretical photovoltaic potential (6.8–7.05 kWh/m²), while 47.27% falls within a very high potential range (6.6–6.8 kWh/m²). For DNI, values vary between 3.76 and 7.29 kWh/m², with 1.45% of the land categorized as globally top-tier potential (7.2–7.75 kWh/m²), and 15.38% classified as high (6.7–7.2 kWh/m²). DIF values range from 1.83 to 2.76 kWh/m², with 38.5% of the area concentrated within the 2.1–2.2 kWh/m² range. GTI values vary between 5.66 and 7.27 kWh/m², where 13.64% of the country is classified under high irradiance (7.1–7.3 kWh/m²). Regarding practical photovoltaic output (PV_OUT), around 2.35% of Yemen’s area falls within the highest operational efficiency class (5.6–5.9 kWh/kWp), and 24.41% is classified as very high (5.4–5.6 kWh/kWp), while 90.09% of the land area achieves at least 5 kWh/kWp. Based on the three evaluation levels (0, 1, and 2), the assessed areas are 455,112 km², 235,748 km², and 219,364 km² respectively, representing 86.2%, 44.65%, and 41.54% of the total land area of Yemen (527,970 km²). Regions with PVOUT values above 5.6 kWh/kWp cover approximately 1.7–2% of the area, while the area exceeding the national average of 5.2 kWh/kWp amounts to 152,007 km². These findings highlight the abundance of solar resources in Yemen, demonstrating the country's strong potential for large-scale solar energy deployment. This supports the development of strategic, sustainable national energy plans aimed at electricity generation and long-term energy independence.

Antimicrobial agents encompass a wide range of compounds, including antibiotics, bacteriocins, and lipopeptides, which play a crucial role in combating infectious diseases. Antibiotics, in particular, are secondary metabolites of low molecular weight predominantly synthesized by soil dwelling microorganisms. These microbial metabolites have long served as a vital source of clinically important therapeutic agents. Members of the genus Bacillus and other rhizosphere-associated bacteria are especially known for producing diverse antimicrobial substances. In this study, ten rhizosphere soil samples were collected from different sites in Sana’a city, Yemen. From these samples, 50 antibiotic-producing soil bacteria were isolated. Bioactive metabolites were extracted using the solvent extraction method with chloroform and Ethanol. The crude extracts were analyzed by Gas Chromatography–Mass Spectrometry (GC–MS) to identify their organic composition. Among the 50 bacterial isolates, 14 showed antibacterial activity against resistant Escherichia coli using the agar well diffusion method. Further secondary screening revealed that the filtrates of four isolates Pseudomonas fluorescens, Bacillus subtilis, Acinetobacter baylyi, and Azotobacter vinelandii exhibited the strongest antibacterial effects. GC–MS analysis showed that each isolate produced more than eighty organic compounds; however, only a subset demonstrated antibacterial activity. The most notable bioactive compounds detected included Phenol, 4-(2-aminoethyl)- (CAS: tyramine), 2,4-di-tert-butylphenol, and n-hexadecanoic acid. This study highlights the potential of rhizosphere soil bacteria as promising sources of novel bioactive compounds. The identification of active metabolites and their antibacterial properties against resistant E. coli underscores their possible application in the development of alternative therapeutic strategiesto address antibiotic resistance.

Generative adversarial networks (GANs) represent a ground-breaking advancement in artificial intelligence, revolutionizing data generation and content creation. This systematic review critically analyses literature from 2019-2024, sourced from major databases like IEEE Xplore and Scopus, to go beyond descriptive surveys and provide a synthesized, critical evaluation. We identify a pivotal gap in the existing literature: a frequent disconnect between reporting technical advancements and examining their profound ethical and societal implications. Our analysis yields several integrated insights. First, while GAN applications predominantly excel in visual content generation with architectures like StyleGAN and BigGAN achieving remarkable progress in photorealistic image and video synthesis their efficacy is highly application-specific, revealing significant trade-offs in stability and diversity in domains such as medical imaging and real-time video processing. Second, persistent core challenges like training instability and mode collapse continue to drive architectural innovations; however, we critically examine how solutions like Wasserstein loss or minibatch discrimination demonstrate varying effectiveness and limitations across different tasks and datasets. Third, and most critically, we argue that ethical concerns, particularly regarding deepfakes and data bias, are not peripheral issues but are intrinsically linked to architectural choices and the quality of training data. The key contribution of this review is its novel, integrated framework for evaluating GANs, which concurrently assesses architectural efficacy, application-specific maturity, and associated ethical risks. This holistic and critical synthesis provides researchers with a nuanced reference and outlines clear, responsible directions for future GAN development, emphasizing the need for models that are not only more powerful but also more robust, fair, and accountable.

Predicting customer churn in retail banking is essential for sustaining profitability. This study compares four supervised machine-learning models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN)—using the publicly available bank customer churn dataset from Kaggle (10,000 records, 18 attributes; publicly available at Kaggle repository. Data preprocessing included one-hot encoding for categorical variables, label encoding for gender, and feature selection via an ExtraTreesClassifier retaining nine informative predictors (e.g., age, credit score, balance). To address class imbalance (≈80% nonchurners vs. 20% churners), models were trained and evaluated with and without the Synthetic Minority Oversampling Technique (SMOTE), which was applied only to the training folds under stratified 5-fold cross-validation. Evaluation metrics comprised accuracy, precision, recall (for churn class), F1-score, ROC–AUC, and PR–AUC. RF achieved the best balance between recall (0.484 imbalanced; 0.619 balanced) and accuracy (0.867 imbalanced), while LR with SMOTE attained the highest recall (0.715) at the cost of reduced accuracy (0.718). Overall, the results highlight RF as the most robust model across both distributions and emphasize the importance of imbalance-aware evaluation in bank churn prediction.

The rapid development of various advanced technologies, including the Internet of Things (IoT), coupled with users’ heavy reliance on technology in various aspects of their daily lives, has led to an increase in the number of devices connected to the Internet. As a result of this rapid growth, the amount of data generated will increase significantly, as the Internet of Things covers many areas, from industrial and healthcare sectors to smart cities and smart homes. However, many challenges, attacks, vulnerabilities, and various anomalies related to the security of IoT devices arise, negatively impacting individuals and organizations. Several anomaly detection techniques have emerged, including machine learning and deep learning, which in turn detect anomalies. This enhances the security, integrity, reliability, and effectiveness of IoT systems. This paper provides a comprehensive survey of peer-reviewed articles from 2018 up to the present that focus on machine learning and deep learning in anomaly detection and attacks on various layers of the Internet of Things architecture. The survey results provide potential insights and recommendations for future research endeavors.

Because of the enhanced efficiency and value that come with implementing ERP systems and e-government, these systems have taken center stage in government organizations throughout both developed and developing nations. To provide more efficient government and better services to citizens, public administrations and agencies have invested in Enterprise Resource Planning (ERP) systems as their core technological infrastructure for e-government. Despite recognizing the importance of management information systems in electronic government, most Arab countries in the Middle East and other underdeveloped countries are still dealing with issues with adopting these technologies. This study introduced a Systematic Literature Review (SLR) to identify the Critical Success Factors influencing the adoption of GRP systems, the benefits of GRP systems, and their challenges. Studies from 2015 to 2024 were selected from databases such as Scopus and IEEE Explore. This study proposed a conceptual model based on technological, organizational, and environmental factors. The study also analyzes the importance of the CSFs. It also summarized the most significant challenges and difficulties in adopting GRP systems, as well as future trends, thereby creating numerous opportunities to develop various solutions.