Sana'a University Journal of Applied Sciences and Technology

We report a highly efficient method for synthesizing carbamatoalkyl naphthols via one-pot, three-component condensation involving 2-naphthol, aldehydes, and butyl carbamates. This reaction, facilitated by cerium ammonium nitrate, can be performed under solvent-free conditions at 80°C and generally occurs within a short time frame. The resulting compounds were thoroughly characterized by FT-IR, ¹H NMR, ¹³C NMR, and mass spectrometry. Additionally, the antimicrobial properties and biocompatibility of the newly synthesized compounds were evaluated using laboratory tests. The findings indicated that these compounds exhibited significant biological activity compared to standard antibiotics, and their hemocompatibility was confirmed.

Over time, various architectural trends evolved and became categorized as contemporary architecture. Despite the existence of contemporary mythological architectural examples, a discernible vision for integrating the mythological fourth dimension into contemporary architecture is notably absent, which constitutes the core research problem. Consequently, the aim of the research focuses on proposing a vision for employing the Mythological Fourth Dimension in contemporary architecture. To achieve this aim, the descriptive method was adopted to analyze theoretical concepts, the analytical method was used to illustrate the mythological fourth dimension in buildings from various contemporary architectural currents and the comparative method was employed to compare the mythology of buildings within contemporary architectural currents with mosque architecture. This process led to the proposal of a vision that employs the fourth mythological dimension into contemporary architecture, culminating in results and recommendations that contribute to the application of the proposed vision. Among the most significant findings is that architectural projects incorporating the mythological fourth dimension gain exceptional tourist appeal and stimulate the local economy through tourism. Furthermore, integrating the mythological fourth dimension into contemporary architecture is an effective strategy for revitalizing and disseminating a community's heritage, values, and beliefs.

Computational design is a computer technology that has contributed to data processing using a set of
parameters and algorithms to meet contemporary design challenges. However, traditional methods are still
the most widely used today in the design process. Accordingly, the research problem is embodied in the
lack of knowledge about the effectiveness of computational design in the processes of generating, exploring,
and developing creative output in contemporary urbanism. The research aims to identify the formulas of
computational design and their effectiveness as an enriching medium for creativity and innovation in urban
production. To achieve the goal, the research follows an inductive approach to clarify the cognitive reality
of previous studies on two levels. The first targeted the formulas and processes of computational design,
and the second discussed creativity and its levels in the achieved output for the purpose of extracting
vocabulary and measurement indicators to test hypotheses on selected urban samples. The study concluded
that computational design is effective as an enriching medium for creativity and innovation in contemporary
urban production, with varying characteristics and levels, ranging from simple creativity to very complex
creativity, depending on the goal, the process followed in the design, and the adopted intellectual reference.

This study evaluated soil and plant contamination by heavy metals (Fe, Cu, Zn, Mn) in the Sana’a Basin,
Yemen, resulting from irrigation with well water located near three pollution sources: (A) the municipal
landfill in Al-Azraqain, (B) sewage effluents in Bani Al-Harith, and (C) stormwater runoff in Al-Rawdah.
Four wells were sampled per site, and a pot experiment using radish (Raphanus sativus L.) was conducted
during the spring season (February–April) to assess enrichment, transfer, and bioaccumulation factors.
Results revealed that Fe and Mn were most concentrated in wells near the landfill (A), likely due to
ferrous and metallic waste, while Cu was highest in Bani Al-Harith (B), possibly linked to coppercontaining materials and pipelines. Zn levels peaked in Al-Rawdah (C), attributed to stormwater transport
across agricultural soils treated with Zn-containing fertilizers and pesticides. Although none of the
detected concentrations exceeded permissible irrigation water limits, soil contents of Fe and Mn increased
substantially after cultivation (279% and 245% of initial values, respectively), whereas Cu and Zn rose by
32% and 59%.
Enrichment factor (EF) analysis indicated Cu accumulation in soils irrigated with water from all wells.
Pollution sources significantly influenced Mn concentrations in both shoots and roots, Fe in roots only,
and Zn variably between shoots and roots depending on the site. Radish exhibited effective Zn
accumulation in both shoots and roots under irrigation with water from site B, but only in roots under site
A, suggesting its potential as a bioindicator for Zn-contaminated soils.
Translocation factor (TF) results showed that radish efficiently absorbed and translocated Cu from roots to
shoots at site A, but not at sites B or C. Bioaccumulation factor (BAF) values in radish roots followed the
order Zn (1.18) > Cu (0.14) > Mn (0.13) > Fe (0.04). The findings highlight a potential human health risk
associated with heavy metal accumulation in edible plants and their entry into the food chain.

Vachellia flava, synonym Acacia ehrenbergiana, known as “Salam” in Arabic, is a wild flowering tall tree belong to Fabaceae family. In Yemen, it thrives in specific ecological niches and traditionally utilized for charcoal and Qataran production, firewood and apiculture, yielding high-quality honey. This study aimed to conduct the first qualitative analysis of the phytochemical constituents of different solvent extracts of the leaves, bark, and pods of Yemeni V.flava, alongside the aqueous solution of its produced honey. The plant parts were subjected to serial exhaustive extraction using petroleum ether, acetone, and methanol using Soxhlet extraction. The obtained extracts were subjected to the test of several phytochemical classes following the standard procedures. Phytochemical screening revealed distinct profiles across extracts. The petroleum ether leaves extract contained exclusively phytosterols, triterpenes, and terpenoids, whereas methanol and acetone extracts shared alkaloids, glycosides, flavonoids, phenolics, carbohydrates, proteins, and fatty acids with methanol uniquely containing saponins and acetone exhibiting amino acids and phytosterols. Bark petroleum ether extract showed glycosides, phytosterols, triterpenes, and terpenoids, while methanol and acetone extracts both contained alkaloids, anthraquinones, glycosides, flavonoids, phenolics, proteins, amino acids, and fatty acids; methanol included saponins, and acetone had additional carbohydrates and phytosterols. Pods petroleum ether extract contained alkaloids and phytosterols, while methanol and acetone extracts featured glycosides, carbohydrates, flavonoids, phenolics, proteins, amino acids, and phytosterols while methanol extract alone contained anthraquinones. Analysis of aqueous honey solution detected only alkaloids, carbohydrates, phenols, flavonoids, and proteins. Notably, petroleum ether extracts of bark and pods contained glycosides and alkaloids, respectively, suggesting the presence of nonpolar forms of these metabolites in Yemeni V. flava. The findings demonstrate that both V. flava and its honey harbor a diverse array of bioactive phytochemicals with potential applications in medicinal and industrial sectors.

Fingerprint recognition has widespread security applications owing to its uniqueness, permanence, and simplicity in capture. However, conventional fingerprint authentication systems face high false acceptance and rejection rates and susceptibility to spoofing attacks. To address these issues, this study proposes a deep learning-based fingerprint authentication system using a Convolutional Neural Network (CNN) with five convolution layers to derive robust spatial features. The model was trained and cross-validated on the SOCOFing dataset with regularization and data augmentation to enhance generalization and spoof resistance. Experimental results show that the proposed CNN achieved a training accuracy of 99.10% with a loss of 0.0223 and a validation accuracy of 98.89% with a loss of 0.0114. Moreover, the model maintained a low false acceptance rate of 0.33% and false rejection rate of 0.25%, demonstrating its efficacy and credibility for secure and real-time biometric authentication. A rigorous comparison with conventional CNN models and DCCN architectures confirmed that the proposed model provides higher accuracy, lower computational cost, and stronger resistance to spoofing attacks. These findings indicate that the proposed system successfully addresses existing limitations and offers a practical, scalable, and reliable solution for fingerprint verification using deep CNN architectures.

Background: Helicobacter pylori (H. pylori) is a bacterium frequently contracted during childhood and is the primary etiological agent of various gastrointestinal conditions, including gastritis, peptic ulcers, and gastric can- cer. The transmission of H. pylori predominantly occurs through person-to-person contact, most commonly via oral-oral or fecal-oral pathways. Its acquisition and persistence are strongly affected by various environmental and socioeconomic conditions. This study aimed to determine the prevalence of H. pylori infection and evaluate its association with multiple risk factors among individuals presenting with gastrointestinal disorders in Dhamar, Yemen. Methods: This cross-sectional study was conducted between January to December 2022. H. pylori infection was detected using the stool antigen test (HpSA), which qualitatively identifies H. pylori antigens in human fecal samples. Demographic and behavioral data including age, gender, smoking status, family size, number of rooms in the household, educational level, Khat (Catha edulis) consumption, and tobacco (Shamma) chewing were collected through structured interviews. Logistic regression models were used in the analysis of odds ratios (ORs) and their respective 95% confidence intervals (CIs), with a 5% significance level. Results: A total of 515 stool samples were collected from individuals in Dhamar Governorate, Yemen, to evaluate the prevalence of H. pylori infection. The overall infection rate was found to be 68.5%, corresponding to 353 positive cases. Among the 185 male participants, 107 (57.8%) tested positive, while 246 of the 330 female participants (74.5%) were positive. Although no significant association was found between infection rates and family size, statistically significant correlations were observed with several factors, including gender, age, number of rooms in the household, educational level, smoking status, khat chewing, and tobacco (Shamma) use. Conclusion: This study highlights the substantial prevalence of H. pylori infection in the Dhamar community. A comprehensive understanding of the associated risk factors is essential for devising effective strategies to control and ultimately eliminate the transmission of this pathogen within the Yemeni population.

A GIS-based Soil Conservation Service Curve Number (SCS-CN) approach was used to estimate surface-runoff potential in Yemen’s arid Abyan Delta (∼1,237 km²). Sentinel-2 (10 m) imagery was classified into major land-cover types and combined with hydrologic soil groups (HYSOGs) to assign CN values under Antecedent Moisture Condition II. The composite CN map (67–100; mean = 91.8) indicates limited infiltration across large areas of bare or impervious surfaces on clayey soils. Using the standard SCS-CN equations, event runoff was simulated for representative storm depths of 74, 124, and 174 mm. The model produced direct-runoff depths of ∼52, 100, and 150 mm, corresponding to runoff coefficients of ∼71%, 81%, and 86%, and basin-wide volumes of 0.65, 1.24, and 1.85 × 10⁸ m³, respectively. Spatial attribution shows that high-CN zones (notably barren HSG-C/D units and built-up/roads) generate most of the runoff despite covering a smaller fraction of the delta. The results identify priority areas for spate-irrigation, retention, and recharge interventions, and provide a reproducible baseline for flood-risk reduction and water-harvesting planning in data-scarce drylands. The workflow can be readily updated with improved soils, land-use, or climate inputs to support adaptive management.

The international trade of livestock plays a significant role in the global dissemination of transboundary animal diseases and associated crisis risks. Brucellosis, a silent zoonotic transboundary disease, affects most live- stock species. This study investigated the prevalence of brucellosis among camels, cattle, sheep, and goats exported from Djibouti and Somalia to Yemen. From April 2024 to April 2025, 1500 blood samples were tested using WOAH-officially recognized sero agglutination techniques. Overall, 34 of 1500 samples (2.26%) tested positive for Brucella. Our analysis revealed significant differences in brucellosis positivity across animal types, age groups, and origins. Cattle showed the highest positivity rate, with 24 of 375 samples (6.4%). In contrast, sheep had 6 positive samples (1.6%), goats had 4 (1.1%), and camels had no positive samples (0%). When examining age, the older group (2-3 years) exhibited the highest prevalence, with 17 of 408 samples (4.2%). The positivity rates varied slightly across seasons, peaking in spring (3%) and autumn (2.6%), with lower rates in summer (2.2%) and winter (1.3%). Regarding origin, the Hirshabella district recorded the highest positivity in 13 of 125 samples (10.4%). This was followed by Southwest Somalia (5.5%) and Jubaland (4.4%). This study represents the first reported index of brucellosis among animals exported to Yemen, underscoring a significant risk, particularly for cattle and older animals. These findings highlight the urgent need for comprehensive epi- demiological investigations to provide policymakers with the data necessary to formulate effective regulations, thereby reducing the spread of this transboundary animal disease.

Linguistic annotation provides additional information associated with a particular purpose in a document or another piece of information. It is widely used in various fields, ranging from computing and bioinformatics to psychology, law, and linguistics. This study aimed to develop a linguistic annotator for nominal Arabic sentences. The annotator detects both the Inchoative (I) and Predicate (P) phrases within nominal sentences in Arabic text. This is based on a shallow parsing method for chunking sentence constituents. The motivation behind this study is to produce a tool that enables further analysis, such as rhetorical parsing. This annotator can help improve the accuracy of linguistic parsers and enhance natural language understanding. The experiments were conducted on a standard dataset supported by a competitive case study. These results are promising and encouraging.

Using the Huygens-Fresnel diffraction integral, the electric field distribution of the Laguerre-Bessel-Gaussian (LBG) beam in a chiral medium is analytically expressed. The findings demonstrate that the LBG beam is divided into two parts: a right circularly polarized (RCP) beam and a left circularly polarized (LCP) beam. The chiral parameter, wavelength, and orders of the Bessel and Laguerre beams are affected by different propagation trajectories. The left circularly polarized beam was observed to accelerate more quickly than the right circularly polarized beam during propagation, and this acceleration was dependent on the chiral parameter. With an increase in the chiral parameter, the RLP beams decreased, and the LCP beams accelerated. The results of this investigation will be useful in optical manipulation and sorting applications.

Meat products are very perishable foodstuff that can easily be contaminated by microbial organisms and spoiled due poor storage and handling facilities. The current research was conducted to determine microbiological quality of imported meat products in Yemeni retail market with emphasis on use of Pseudomonas spp. to test the level of spoilage. A total of 214 samples that included beef burgers, chicken burgers, chicken meat, minced beef and minced lamb were aseptically collected at the 2 supermarkets in Sanaa in Yemen. Each sample was analyzed by standard culture-based procedures to achieve total aerobic plate count (APC), and presence of Pseudomonas spp., followed by species-level identification with the help of VITEK(R) 2 Compact system. The number of minced beef samples and minced lamb samples had the highest mean APC values of 8.87 10 5 and 8.55 10 5 trend, respectively, lower values were found in burger samples and chicken meat samples. The percentage rates of detection of Pseudomonas spp. were 16.36 with the maximum percentages found in minced lamb (35.29) and minced beef (25.00). Pseudomonas aeruginosa (60ATTR Morphological identification of Pseudomonas spp. showed that P. aeruginosa was the commonest isolate (60%) followed by P. fluorescens (40%). The samples were all compliant with the Yemeni GSO 1016:2019 and 83.64 percent with the ISO 13720:2010, which states that the microorganisms must not have Pseudomonas spp. Such results support the increased susceptibility of minced meats towards microbial contamination and putrifaction. In revising this study, the researchers suggest that they put Pseudomonas spp. monitoring as part of the national food safety policy and strengthening the cold chain to increase meat quality control and safeguard the health of the people in Yemen.

Strong cybersecurity measures are becoming vitally crucial as cloud computing utilization rises. Advanced and dynamic cyberthreats are frequently difficult for traditional intrusion detection systems (IDS), which rely on preset signatures and rules, to identify. This study improves the detection of known and unknown intrusions in cloud systems using Machine Learning (ML) methods. The UNSW-NB15 dataset was used to train and assess a num- ber of ML classifiers, including Random Forest (RF), Decision Tree (DT), XGBoost, Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Gradient Boosting (GB). It uses full feature training across several classifiers and also investigates the implications of feature reduction, in contrast to many other studies that mainly employ full feature sets to train RF alone. Critical metrics including accuracy (ACC), precision, recall, and F1-score are used to analyze classifier performance and provide a thorough evaluation of their efficacy in in- trusion detection. The findings show that using all characteristics the RF and DT obtained perfect accuracy (1.00). In the case of less characteristics when using feature selection techniques (RF-based selection, information gain, or mutual information), the RF retained the best accuracy (0.94), whereas NB performed the worst overall. This study emphasizes the significance of feature selection in enhancing IDS performance and shows that ML-based techniques may greatly increase threat detection in cloud settings, even when feature dimensionality is lowered.

Theoretically, this study investigates the optical forces induced by a pulsed LG_0l modes on nano-dielectric spheres with low and high refractive indices. We analyze how trapping efficiency depends on the particle’s refractive index and the beam’s azimuthal mode in terms of force magnitude, effective trapping region, and the number of trapping zones. The theoretical model considers dielectric spheres immersed in an oil medium and irradiated by the beam. The results demonstrate that a pulsed LG_0l beam achieves higher and more stable trapping efficiency for small azimuthal mode values, short pulse duration, and particles with high refractive indices. Theoretical analysis suggests that the LG_0l mode enables 2D trapping of particles with bottle-beam-like control.

 Hodeidah City, the main port on Yemen’s western coast, is under severe healthcare strain owing to ongoing conflicts and humanitarian crises. Government hospitals operate under pressure with limited data on healthcare workers' adherence to medical precautions. This study aims to explore awareness and safety practices. It uses descriptive and analytical methods to assess healthcare workers’ compliance with medical waste management guidelines, identify knowledge and practice gaps, and evaluate institutional adherence to national and international regulations. Data were gathered through a semi-structured, self-administered questionnaire that was administered to 333 healthcare workers in ten medical institutions in Hodeidah City from January 2022 until January 2023. The present study highlights major gaps in medical waste management and the protection of cleanliness workers in healthcare settings. While medical staff generally follow waste segregation protocols within structured systems, cleanliness workers face challenges such as poor training, limited supervision, inadequate protective gear, and vaccination. Issues such as torn waste bags and lack of medical checkups further expose systemic vulnerabilities. Despite this, many clean workers reported high job satisfaction, reflecting resilience and dedication. To improve safety, healthcare facilities must strengthen their oversight, provide consistent protective equipment, and offer regular health assessments and targeted training. These steps are essential for safeguarding workers, the public, and the environment and for building a more resilient healthcare system in Yemen.