Current Issue

Sana'a University Journal of Applied Sciences and Technology

Vol. 4 No. 5 (2026)

2026-05-28

9 Articles

The fourth volume – the fifth issue in the Sana'a University Journal of Applied Sciences and Technology

Article

8 Articles

Prevalence of abscess disease in sheep and goats according to age

Clinical Features, Prevalence, and Bacterial Etiology of Abscess Disease in Sheep and Goats in Sana’a Province, Yemen

Abdulraqeb Alshami, Hamid Ali Alrefaiey, Saleh A. M. A. ALomaisi, Abdu-Alraoof Al-shawkany, Bashar Saleh Al-Mawti, Faiq Abdo Obaid, Sondos Mohammed Anaam

2034-2041

2026-05-28

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DOI: 10.59628/jast.v4i5.2776

Abstract

Abscess disease (caseous lymphadenitis and Morel’s disease) is a chronic bacterial infection affecting sheep and goats, causing significant economic losses in small ruminant production worldwide. This study investigated the prevalence, clinical features, and bacterial etiology of abscess disease in sheep and goats in Sana’a Province. A total of 6,119 sheep and 2,974 goats were examined from March 2023 to February 2024. Clinical examinations were performed, and data on species, sex, age, and abscess location were recorded. Additionally, 86 pus samples (55 from sheep and 31 from goats) were collected for bacteriological analysis. Data were analyzed using SPSS version 25, and associations were evaluated using chi-squared (χ²) test at P<0.05. The overall prevalence was 7.51%, with higher rates in sheep (7.82%) than goats (6.86%). Males showed significantly higher infection rates than females (P<0.05). Younger animals (<1 year) had the highest prevalence, followed by those aged 1–2 years, while animals older than four years had the lowest rates (P<0.05). Clinically, animals exhibited superficial lymph node abscesses of varying sizes, sometimes associated with wool or hair loss, while vital signs remained normal. The most commonly affected lymph nodes were parotid and prescapular in sheep, and prescapular, precrural, and parotid in goats. Bacterial growth was detected in 82.55% of samples. Staphylococcus aureus subsp. anaerobius was predominant isolate (40.84%), followed by Corynebacterium pseudotuberculosis (14.08%). Other pyogenic bacteria were identified, including S.aureus, Streptococci, P.aeruginosa, A.pyogenes and Proteus. These findings provide important epidemiological and clinical insights to support effective control strategies for abscess disease in Yemen.

Keywords

Caseous lymphadenitis Morel’s disease Staphylococcus aureus subsp. anaerobius Corynebacterium pseudotuberculosis Sheep Goats Yemen.

Blockchain-Enabled Clustering for Dynamic Resource Allocation and Task Offloading in UAV-Assisted MEC for 5G Network Slicing

Asrar Ahmed Baktayan, Ammar Thabit Zahary, Ibrahim Ahmed Al-Baltah

2049-2063

2026-05-28

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DOI: 10.59628/jast.v4i5.2655

Abstract

5G network slicing, Unmanned Aerial Vehicle (UAV) aided Multi-Access Edge Computing (MEC), and blockchain technology create new options for intelligent resource management. However, a major difficulty is efficiently allocating limited UAV resources to heterogeneous device demands while guaranteeing slice-specific Quality-of-Service (QoS) criteria. We develop a blockchain-empowered clustering framework that couples seven clustering algorithms (K-Means, DBSCAN, OPTICS, MeanShift, Hierarchical, Gaussian Mixture Model, and Divisive clustering) with an upgraded contextual bandit decision agent. The agent dynamically chooses between local execution and UAV offloading depending on slice characteristics, signal strength, latency constraints, and cluster homogeneity parameters. We use dual smart contracts (UserAllocation and UserOffload) on a permissioned Ganache (Ethereum) blockchain to ensure trust and transparency. An appropriate model of UAV placement considers the signal propagation effects and spatial distribution. The experimental results demonstrate that our clustering-aware method achieves significant improvement over non-clustered baselines by 27% in task offloading reward, 43% in regret reduction, and 20% in action accuracy, while maintaining high SLA requirements for all slice types (URLLC, eMBB, mMTC). The best overall performance is obtained with the MeanShift method. Dynamic device clustering is not only a data organization approach but also an excellent control mechanism for slice-aware resource orchestration in UAV-MEC systems. The combination of clustering intelligence, contextual bandit decisions, and blockchain-based trust gives a scalable, adaptable, and accountable solution for 5G/6G UAV-assisted edge networks.

Keywords

UAV-MEC task offloading Blockchain Smart contract Ganache Ethereum Truffle 5G net- work slicing Dynamic Clustering Contextual Bandit.

The proposed multi-label classification architecture for Qur’anic similes, illustrating the flow from the input verse through the Transformer encoder to the independent sigmoid output layer.

Multi-Label Classification of Qur’anic Similes: A Computational Approach to Arabic Rhetorical Theory

Wadee A. Nashir, A S. Al-Hegami, B. Al-Fuhaidi, Wedad AL-Sorori, Naseebah Maqtary

2064-2074

2026-05-28

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DOI: 10.59628/jast.v4i5.2677

Abstract

Similes (tashb¯ıh) serve as a cornerstone of Qur’anic eloquence, functioning as a vital cognitive tool for conveying complex theological concepts through accessible imagery. Despite the sophisticated taxonomies established by classical Arabic rhetoricians, contemporary computational approaches often rely on single-label classification paradigms. This methodological reductionism fails to capture the inherent “rhetorical overlap” where a single verse embodies multiple, non-mutually exclusive categories, such as being simultaneously explicit (mursal) and representational (tamth¯ıl¯ı). This study addresses this gap by formalizing Qur’anic simile classification as a multi-label learning task, bridging the divide between classical linguistic theory and modern Natural Language Processing. Utilizing an expert annotated dataset of 364 verses grounded in authoritative classical exegeses, we evaluated the performance of several Arabic-specific Transformer models, including AraBERT, CamelBERT, and MARBERT. Quantitative results demonstrate that MARBERT achieved superior performance, reaching a Micro F1 score of 0.7685 and a Macro F1-score of 0.6003, significantly outperforming traditional statistical baselines. The findings revealed a high label density across the corpus, providing empirical validation for the synergistic and multidimensional nature of Qur’anic rhetorical figures. Beyond technical metrics, this study contributes to “computational hermeneutics” by demonstrating that classical rhetorical categories function as structured, learnable knowledge. By successfully modeling overlapping categories, this study offers a novel methodology for Digital Humanities and provides a scalable framework for the automated analysis of highly sophisticated Classical Arabic texts.

Keywords

Arabic NLP Qur’anic Rhetoric Simile Classification Multi-Label Learning Computational Rhetoric Transformer Models Classical Arabic

Geological map of the study area and its surroundings [20].

Evaluation of Crushed Basaltic Rocks as Coarse Aggregate, at Selected Site South Sana’a, Yemen: Properties and Concrete Performance

Ahmed M. Al-Anweh, Ibrahim A. Al-Akhaly

2075-2087

2026-05-28

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DOI: 10.59628/jast.v4i5.2366

Green Synthesis, Characterization, and Biological Evaluation of Silver Nanoparticles Using Rumex nervosus Flower and Fruit Extracts

Reham Aref, Hassan.M. Ibrahim, Maher Ali Almaqtari, Esam M. Aqlan

2088-2102

2026-05-28

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DOI: 10.59628/jast.v4i5.2764

Abstract

Green nanobiotechnology represents a sustainable and cost-effective platform for synthesizing metallic nanoparticles using biologically driven mechanisms. In this study, silver nanoparticles (AgNPs) were biosynthesized for the first time using methanolic and ethanolic extracts of Rumex nervosus flowers and fruits. Phytochemical profiling confirmed the presence of phenolics, flavonoids, tannins, and related secondary metabolites in R.nervosus flower and fruit extracts, which served as intrinsic reducing and stabilizing agents for AgNP synthesis. The formation of Silver Nanoparticles was indicated by a distinct color transition and further verified by ultraviolet–Visible Spectroscopy, which revealed characteristic surface plasmon resonance. Fourier transform infrared spectroscopy demonstrated notable shifts in the O–H, C=O, C–O, and C–N vibrational bands in the extracts, confirming their involvement in metal ion reduction and capping. X-ray diffraction analysis revealed a face-centered cubic crystalline structure with an average crystallite diameter ranging from 20.7–23.9 nm. Antioxidant evaluation via the 2, 2-diphenyl-1-picrylhydrazyl assay showed that AgNPs exhibited superior radical-scavenging capacity compared to crude extracts, with methanolic AgNPs demonstrating the highest activity (91.69%). Antimicro- bial testing revealed that ethanolic AgNPs displayed significant inhibitory activity against gram-negative bacteria, whereas methanolic AgNPs showed negligible effects owing to enhanced nanoparticle stabilization and reduced Ag + availability. These results highlight that R. nervosus is an effective biomediator for AgNP formation with promising antioxidant and antibacterial applications.

Keywords

Green synthesis Rumex nervosus Flowers and fruits Silver Nanoparticles Characterization Antioxidant Antimicrobial

الموقع الجغرافي لقلعة صيرة في مدينة عدن (اليمن) كمصدر لعينة القضاض المدروسة.

Unveiling the Secrets of Ancient Waterproofing: A Multi-Analytical Characterization of Traditional Qadhad Mortar from Sira Castle, Yemen

Sarhan Al-Gawi, M.A. Al-Gorafi, Abdulwahab M. Al-Nono

2103-2121

2026-05-28

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DOI: 10.59628/jast.v4i5.2910

Abstract

Qadhad is one of the most prominent traditional construction materials in Yemeni architecture, it has been used for centuries in lining cisterns, roofs, and hydraulic structures due to its high resistance to water and moisture, as well as its remarkable long-term durability. This study aims to characterize and analyze the properties of traditional Qadhad used in the historic Sira Castle in Aden City, thereby providing a scientific basis for its reproduction with comparable performance in contemporary conservation and construction as a waterproofing material. Physical tests (apparent density, open porosity, water absorption, and capillary absorption), hydraulic tests (permeability and hydraulic indices), and mineralogical analyses (XRD, XRF, SEM-EDS) were conducted in accordance with international standards. The results indicated that the apparent density of the Sira Castle Qadhad reached 1740 kg/m³, while the open porosity was 19.6%. The water absorption was 11.27%, and the permeability coefficient was approximately 6.55×10⁻⁹ m/s, demonstrating an exceptional ability to resist water infiltration and an excellent balance between density and durability. The mineral analyses (XRD and XRF) revealed the mineralogical and chemical composition of the Qadhad, while SEM-EDS micrographs showed its dense microstructure formed by calcium and aluminum hydrate gels that fill the pores, granting the material outstanding waterproofing performance that has endured for centuries in coastal environments.

Keywords

Yemeni plaster Traditional building materials Waterproofing material Characterization XRD XRF SEM

Charts of annual rainfall data for some rain gage stations in Sana’a Basin

Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Hadi A. Quriaa, Bassim S. Al Khirbash, Hisham M. Nagi, Amer M. Al-Sabri

2122-2129

2026-05-28

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DOI: 10.59628/jast.v4i5.2859

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Commentary Comment on The Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Response Reply To Comments On The Spatiotemporal Analysis of Rainfall Variability in the Sana’a Basin, Yemen: An Integrated Remote Sensing and GIS Approach

Abstract

Sana’a Basin in the Central Highlands of Yemen is critical economic importance for drinking water, as well as for rainfed and irrigated agriculture. Consequently, and any shift in rainfall patterns profoundly impact both groundwater recharge on the one hand, and crop ecosystems on the other hand. This study aims to analyze precipitation dynamics in the Sana’a Basin, by analyzing data from 26 meteorological stations distributed across the region to understand the temporal and spatial variations in rainfall. The research utilizes datasets from the National Water Authority for the period from 2010 to 2024. The results revealed significant spatial heterogeneity in rainfalldistribution, with the northeastern and central regions experiencing minimal precipitation, while the western and southern areas recorded considerably higher levels. These patterns are largely driven by orographic lifting and atmospheric dynamics. The observed trends indicate that a systematic decline in rainfall at multiple stations suggests prolonged aridification in specific zones, which could detrimentally affect groundwater replenishment, agricultural productivity, and water security. Conversely, localized increases in rainfall at certain stations suggest potential climate-induced shifts that may elevate flood risks or, alternatively, improve water availability. Furthermore, the study validates the effectiveness of integrating satellite based observations (TRMM) to complement field data, particularly in data-scarce regions. These findings underscore the urgent need for continuous monitoring and the implementation of adaptive water management strategies to mitigate the impacts of climate variability on the basin’s fragile water resources.

Keywords

Rainfall data Spatial analysis ArcGIS Sana'a Basin Yemen

PRISMA flow diagram depicting the study selection process.

Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024)

Haron M . Ahmed Omer, Fadl Ba-Alwi

2130-2138

2026-05-28

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DOI: 10.59628/jast.v4i5.2643

Abstract

The widespread adoption of face masks during the COVID-19 pandemic created new requirements for facial recognition technology, driving focused innovation in Masked Face Recognition (MFR) systems. This survey synthesizes and analyzes key research developments from 2020 to 2024, examining more than twenty pivotal studies in the field. The review provides a structured comparison of contemporary deep learning methodologies, with particular attention to Convolutional Neural Network (CNN) implementations including ResNet, VGG, and MobileNet architectures. It further evaluates the characteristics and performance outcomes associated with principal publicly available datasets employed in MFR model development. The analysis highlights important considerations for advancing MFR systems, particularly regarding data quality and model generalization. Current research directions emphasize improving the alignment between training data characteristics and real-world application environments. Among available resources, the MaskedFace-Net dataset demonstrates particularly strong performance in benchmark evaluations, with reported recognition rates exceeding 99%, attributed to its specialized design and substantial scale. For practical implementation, the findings support utilizing established CNN-based frameworks, with opportunities for enhancement through complementary approaches such as integration with traditional classifiers. Looking forward, this survey identifies promising pathways for continued progress in MFR technology, centered on evolutionary advancements within proven architectural paradigms, sophisticated data utilization strategies, and optimizations for deployment efficiency. This comprehensive review offers valuable guidance for researchers and engineers working to create effective and adaptable masked face recognition solutions.

Keywords

Machine learning Deep Learning Face Recognition masked Face Recognition Convolutional Neural Networks.

Review

1 Articles

The integrated conceptual framework for OSS adop- tion and acceptance factors.

Factors Influencing the Acceptance and Adoption of Open-Source Software: A Concept-Centric Systematic Literature Review

Sumaia A. Al-Edresi, Adnan Y. Al-Mutawakkil

2139- 2165

2026-05-28

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DOI: 10.59628/jast.v4i5.2201

Abstract

Free and open-source software (FOSS) is software in which anyone is freely licensed to use, copy, study, and change the software in any way. The source code is publicly shared, encouraging people to improve the software design voluntarily. This is in contrast to proprietary software, where the software is subject to a restrictive copyright license and the source code is usually hidden from users. Many countries are witnessing a significant transition towards adopting open-source software within their government structures, coupled with efforts to develop strategies and policies that support this trend. This change is attributed to several reasons, most notably savings in the total cost of ownership, freedom to copy and distribute software, compliance with legal controls, high reliability, a variety of available programs, and rapid performance, in addition to the multiple educational and administrative benefits. Despite this remarkable expansion, it remains necessary to understand the potential of this software for individuals and entities while identifying the factors influencing its adoption. Accordingly, this study aims to explore the acceptance of open-source software and analyze the technical, administrative, and personal factors that promote its use, especially in environments undergoing a transition towards sustainable digital systems. The study collected several papers published between 2007 and 2023, approximately 85 different research studies combining theoretical and experimental aspects. After that, 61 papers were selected from among them and analyzed. The results revealed six main categories that influence the acceptance and adoption of open-source software. The study recommends developing a comprehensive model that integrates all behavioral, social, cultural, and technical dimensions to identify the factors influencing the acceptance and adoption of open-source software. The study also emphasizes the need for future empirical research in the Yemeni context, especially in public and educational sectors, to validate the integrated framework, and provide evidence-based policy recommendations for effective OSS adoption.

Keywords

adoption open source acceptance usability factors influencing.

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