Research Title: Nurses’ therapeutic nutrition knowledge: A crosssectional survey in Yemen

Author: Faten Abdo Mohammed Hassan, Published Year: 2025

Mal J Nutr, 2

Faculty: Science

Abstract: Introduction: Nutrition plays a critical role in improving the health of individualsstruggling with chronic conditions. Nurses have the potential to reduce morbidityand mortality through effective nutritional counselling and advice. This study aimedto assess level of nurses’ therapeutic nutrition knowledge and factors influencingtheir knowledge levels. Methods: A descriptive cross-sectional survey was conductedbetween December 2023 and February 2024 at two public hospitals in TaizGovernorate, Yemen, using self-administered questionnaire. A convenience sampleof 207 nurses participated in the study. The questionnaire comprised two sections:demographic data and 31 items assessing nurses’ therapeutic nutrition knowledge,divided into diabetes, obesity, and cardiovascular diseases. Results: Total scoresfor nurses’ therapeutic nutrition knowledge ranged from 2 to 23, with mean totalscore of 14.43 out of 31 (46.5%). Only 13 out of 31 items were answered correctlyby more than 50.0% of nurses, while eight items were answered incorrectly by over80.0% of nurses. Only 35.5% of nurses had satisfactory knowledge, while 64.5%had unsatisfactory knowledge. Mean scores for therapeutic nutrition knowledgeregarding diabetes, obesity, and cardiovascular diseases were 2.82 out of 5 (56.4%),4.17 out of 9 (46.3%), and 7.44 out of 17 (43.8%), respectively. Conclusion: Thefindings revealed low level of nutrition knowledge among nurses, emphasising acritical gap that must be addressed. Integrating comprehensive nutrition contentinto nursing curricula and implementing targeted education can bridge this gap.Enhancing nurses’ knowledge will improve patient outcomes and support broaderpublic health goals through more effective nutrition policies (PDF) Nurses’ therapeutic nutrition knowledge: A crosssectional survey in Yemen. Available from: https://www.researchgate.net/publication/392990410_Nurses'_therapeutic_nutrition_knowledge_A_crosssectional_survey_in_Yemen#fullTextFileContent [accessed Nov 30 2025].

Keywords: cardiovascular disease, chronic diseases, diabetes, nurses’ therapeuticnutrition knowledge, obesity

Research Title: Combating the causative agent of amoebic keratitis, Acanthamoeba castellanii, using Padina pavonica alcoholic extract: toxicokinetic and molecular docking approaches

Author: Faten Abdo Mohammed Hassan, Published Year: 2024

Scientific Reports, 14

Faculty: Science

Abstract: Natural products play a significant role in providing the current demand as antiparasitic agents, which offer an attractive approach for the discovery of novel drugs. The present study aimed to evaluate in vitro the potential impact of seaweed Padina pavonica (P. pavonica) extract in combating Acanthamoeba castellanii (A. castellanii). The phytochemical constituents of the extract were characterized by Gas chromatography–mass spectrometry. Six concentrations of the algal extract were used to evaluate its antiprotozoal activity at various incubation periods. Our results showed that the extract has significant inhibition against trophozoites and cysts viability, with complete inhibition at the high concentrations. The IC50 of P. pavonica extract was 4.56 and 4.89 µg/mL for trophozoites and cysts, respectively, at 24 h. Morphological alterations of A. castellanii trophozoites/cysts treated with the extract were assessed using inverted and scanning electron microscopes and showed severe damage features upon treatment with the extract at different concentrations. Molecular Docking of extracted compounds against Acanthamoeba cytochrome P450 monooxygenase (AcCYP51) was performed using Autodock vina1.5.6. A pharmacokinetic study using SwissADME was also conducted to investigate the potentiality of the identified bioactive compounds from Padina extract to be orally active drug candidates. In conclusion, this study highlights the in vitro amoebicidal activity of P. pavonica extract against A. castellanii adults and cysts and suggests potential AcCYP51 inhibition

Keywords: Acanthamoeba castellanii, Amoebic keratitis, Cytochrome P450 monooxygenase, Molecular docking, ADME analysis

Research Title: Antibacterial/radical scavenging activities, content, chemotaxonomy and chemical components of volatile oils of two Plectranthus amboinicus (Lour.) Spreng. (Lamiaceae), grown in Yemen

Author: KHALED HUSSEIN MOHAMMED ALMAAH, Published Year: 2017

American Journal of Essential Oils and Natural Products , Volume 5, No(2)

Faculty: Science

Abstract: Abstract Evaluate the content, identify the chemotype and analyze the chemical components of volatile oils of the cultivated Plectranthus amboinicus (CPSU) and the wild P. amboinicus (WPRR), from Yemen, were the main targets of this work. Antibacterial activity of the investigated oils were tested against four standard bacterial strains. Radical scavenging activity (RSA) of oils were estimated as well, using spectrophotometric DPPH method. Volatile oils components were achieved by IR spectroscopy and GC/MS. The presence of phenolic and alcoholic compounds within the oils components were inferred by IR analysis. GC/MS analysis of CPSU and WPRR oils showed that the dominant component of both is thymol (36.90% and 79.20% correspondingly) and indicated that the examined oils have thymol chemotype. The evaluation of the oil’s content show that the CPSU and WPRR possess 1.43% and 1.57% (v/w) oil content, respectively. Remarkable antibacterial activity and promising RSA of oils, were found to be correlated mainly to thymol and other alcoholic components. RSA results indicated that WPRR oil could be considered as a natural source of hydroxyl radical scavenger.

Keywords: Keywords: Plectranthus amboinicus, volatile oil components, antibacterial activity, radical scavenging activity, thymol

Research Title: Chemotaxonomy and Spectral Analysis (GC/MS and FT-IR) of Essential Oil Composition of Two Ocimum basilicum L. Varieties and their Morphological Characterization.

Author: KHALED HUSSEIN MOHAMMED ALMAAH, Published Year: 2017

Jordan Journal of Chemistry, Vol. 12No.3

Faculty: Science

Abstract: Abstract In this study, we evaluated the content and composition of the essential oils of two varieties of Ocimum basilicum L. grown in Yemen; var. basilicum and var. purpurascens. The quantitative variations in the relative amounts of the main components of essential oils of both varieties were determined using infrared spectroscopy (IR) and gas chromatography/mass spectrometry (GC/MS) in order to identify their chemo types. Out of all components detected, twenty-three components in the purple variety and thirty-one components in the green variety were identified. Linalool (44.37%; 46.24%), estragole (20.05%; 13.26%), trans-methyl cinnamate (15.05%; 0.45%), 1,8-cineole (9.28%; 3.28%) and epi-α-cadinol (1.38%; 3.10%) were identified as the main and common components of both purple and green varieties oils. Unusually high oil content was recorded in both studied varieties with slight variations between them. The morphological features of these varieties were described and presented to give them a definite scientific taxonomy.

Keywords: Phytochemistry; Ocimum basilicum; Essential oil composition; GC/MS; FT-IR; and Chemotaxonomy.

Research Title: Chemical Composition and Biological Activity of the Essential Oil Isolated from the Leaves of Achillea fragrantissima Growing Wild in Yemen

Author: KHALED HUSSEIN MOHAMMED ALMAAH, Published Year: 2019

Pharmacognosy Journal, Vol 11, Issue 5

Faculty: Science

Abstract: ABSTRACT Background: Yemen is diverse in its geography and rich in its natural flora. Achillea fragrantissima grown wild in Yemen is widely used in folkloric medicine. Objectives: To investigate the chemical composition, cytotoxicity, xanthine oxidase inhibitory and tyrosinase inhibitory activities of the essential oil isolated form the leaves of Achillea fragrantissima (Forssk.) Sch. Bip., growing wild in Yemen. Materials and Methods: The oil was collected after hydro distillation for 3 h,, the oil composition was analyzed by GC-MS and assayed for biological activities. Results: Artemisia ketone (49.53%), camphor (14.73%), α-bisabolol (11.20%), α-bisabolol oxide B (2.62%) were the main components of the oil. The MTT assay of the oil on two human colorectal cancer cell lines (SW480 and HCT-116) showed IC50 values of 110.1 and 134.6 µg ml⁻¹, respectively. Xanthine oxidase inhibitory and tyrosinase inhibitory activity assays were performed but exhibited only marginal activities. Conclusion: the components of the essential oil could be excellent anticancer drugs for treatment of colon cancer.

Keywords: Key words: Achillea fragrantissima, Artemisia ketone, Essential oil, Cytotoxicity, GC-MS.

Research Title: Antibacterial Activity of a Novel Oligosaccharide from Streptomyces californics against Erwinia carotovora subsp. Carotovora

Author: KHALED HUSSEIN MOHAMMED ALMAAH, Published Year: 2022

Molecules , 27

Faculty: Science

Abstract: The present study aims to characterize and predict models for antibacterial activity of a novel oligosaccharide from Streptomyces californics against Erwinia carotovora subsp. carotovora using an adaptive neuro-fuzzy inference system and an artificial neural network. The mathematical predication models were used to determine the optimal conditions to produce oligosaccharide and determine the relationship between the factors (pH, temperature, and time). The characteristics of the purified antibacterial agent were determined using ultraviolet spectroscopy (UV/Vis), infrared spectroscopy (FT-IR), nuclear magnetic resonance spectroscopy (1H- and 13C-NMR), and mass spectrometry (MS). The best performances for the model were 39.45 and 35.16 recorded at epoch 1 for E. carotovora Erw5 and E. carotovora EMCC 1687, respectively. The coefficient (R2) of the training was more than 0.90. The highest antimicrobial production was recorded after 9 days at 25 °C and a pH of 6.2, at which more than 17 mm of the inhibition zone was obtained. The mass spectrum of antimicrobial agent (peak at R.T. = 3.433 of fraction 6) recorded two molecular ion peaks at m/z = 703.70 and m/z = 338.30, corresponding to molecular weights of 703.70 and 338.30 g/mol, respectively. The two molecular ion peaks matched well with the molecular formulas C29H53NO18 and C14H26O9, respectively, which were obtained from the elemental analysis result. A novel oligosaccharide from Streptomyces californics with potential activity against E. carotovora EMCC 1687 and E. carotovora Erw5 was successfully isolated, purified, and characterized.

Keywords: Keywords: antibacterial activity, optimization, oligosaccharide, Streptomyces californics

Research Title: A Novel Approach to Feature Model Configuration Using Maximum Independent Set Algorithms

Author: Enas Tawfiq Al-Naffar, Published Year: 2025

International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026, Philippines

Faculty: Information Technology

Abstract: Abstract— Software product line has gained increasing attention in recent years, focusing on creating a set of products that share common functionalities and characteristics. A feature model serves as a key artifact in software product line, providing a graphical representation of all potential features of a software product. When a new product is demanded, it is configured by choosing desirable and coherent features rather than building the product from scratch. The process of generating a valid configuration of a feature model manually can be time-consuming and an error-prone process, especially when dealing with large and complex feature models. In this research, we propose a novel approach to automatically generate a valid and coherent configuration of a feature model, using graph-based methods, based on the solution of the maximum independent set problem. Preliminary validation is done using a case study.

Keywords: Software product line, feature model, configuration, maximum independent set.

Research Title: Modular Monolith Architecture in Cloud Environments: A Systematic Literature Review

Author: Lamis Al-Qoran, Published Year: 2025

Future Internet, 17(11)

Faculty: Information Technology

Abstract: Modular monolithic architecture (MMA) has recently emerged as a hybrid architecture that is positioned between traditional monoliths and microservices. It combines operational simplicity with modularity and maintainability. Although industry adoption of the architecture is growing, academic research on MMA remains fragmented and lacks systematic synthesis. This paper presents the first systematic literature review (SLR) of MMA in cloud environments. The review follows Kitchenham’s guidelines; we searched six major digital libraries for peer-reviewed studies published between 2020 and May 2025. From 369 retrieved records, we included 15 primary studies through a structured review protocol. Our synthesis highlights the problem of inconsistent terminology usage in the literature. It also identifies the architectural scope of MMA, and specifies the adoption drivers such as simplified deployment, maintainability, and reduced orchestration overhead. We also analyse implementation practices—including Domain-Driven Design (DDD), modular boundaries, and containerised deployment—and highlight comparative evidence showing MMA’s suitability when microservices introduce excessive complexity or costs. Key research gaps include the absence of consensus on a clear comprehensive definition, limited empirical benchmarking, and insufficient tools support. Thus, this study establishes a conceptual foundation for future research and provides practitioners with structured insights to inform architectural decisions in cloud-native environments.

Keywords: modulith; service weaver; domain-driven design (DDD); microservices architecture; software engineering modular monolith; cloud computing

Research Title: Numerical Investigation on the Efficiency of Hemp Fiber Composites for Repairing Heat-Damaged Cantilever Beams Using Machine Learning

Author: Sawsan Mohammad Alkhawaldeh, Published Year: 2025

Journal of Soft Computing in Civil Engineering, 10-2 (2026) 1906

Faculty: Engineering and Technology

Abstract: The research examines the use of hemp fiber composites in restoring heat-damaged cantilever beams, applying numerical simulations and machine learning methods. This study investigates the efficacy of ecologically sustainable hemp fiber composites as a repair material using finite element models that have been verified using experimental data. The study emphasizes the capacity of hemp composites to enhance structural efficacy and sustainability in the building field. Using a meticulous technique, this study evaluates the influence of composites on mechanical parameters, including load-bearing capacity and deflection, across different temperature conditions. Machine learning improves predicted accuracy for structural behavior, showcasing an innovative method in structural engineering analysis. This study presents a quantitative approach for assessing the restoration of heat-damaged beams using hemp fiber composites, revealing substantial improvements in structural integrity. The research used finite element modeling to provide precise temperature and load-deflection predictions, eliminating the need for lengthy fire testing. The failure loads of hemp fiber repairs were enhanced by 32.2% and 30.7% at temperatures of 400 °C and 500 °C, respectively. This provides a cost-effective and sustainable option for repairs. In addition, machine learning models, particularly Gradient Boosting and Random Forest demonstrated high predicted accuracy in analyzing structural behavior. This represents a significant and hopeful development in structural engineering and sustainable repair methods.

Keywords: Hemp fiber composites; Heat-damaged beams; Finite element modeling; Structural performance; Machine learning predictions.

Research Title: A Review of CNN-Based Techniques for Accurate Plant Disease Detection

Author: Mahmoud Mohammed Mahmoud Hussein, Published Year: 2023

IJCI. International Journal of Computers and Information , 10

Faculty: Information Technology

Abstract: Abstract— Various techniques have revolutionized the field of plant disease detection, offering accurate approaches for timely detection and recognition of crop diseases. This comprehensive review explores the current utilization of diverse techniques for plant disease detection and classification. It analyzes recent publications, considering aspects such as disease detection methods and dataset characteristics. These techniques have significantly advanced object detection and recognition in agriculture, facilitating efficient crop management and higher yields. However, the complexity of identifying and detecting plant diseases from images necessitates species-specific detection for customized control strategies. This study discusses the challenges and proposed solutions associated with the use of different techniques in early disease detection concentrated on deep learning methods. Overall, the review demonstrates the considerable potential of these techniques in disease detection and emphasizes the ongoing need for research and development to address current challenges and optimize their benefits in agriculture. and also underscores the importance of incorporating emerging technologies and data-driven approaches to further enhance the precision and scalability of plant disease detection systems.

Keywords: Deep learning; CNN; plant disease datasets ; pre-trained models