MSA Repository "MSAR"

MSAR University's Digital Repository is a documentation and digitization of all university outcomes that are of effective value in the scientific and academic community and reflects the university's image, work, and effective contribution to society Through MSAR Digital Repository, the university managed to collect, store, archive and publish digital content - including documents, audio files, images and data sets - all in a safe place. MSAR is one of the strongest University Digital Repositories in Egypt and documented in the DSPACE community with its latest versions.

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RESEARCH & LANGUAGES CENTERS 18

Recent Submissions

Characterization of a Novel α-L-fucosidase from Truepera sp. for efficient transfucosylation and 2’-fucosyllactose biosynthesis
(American Chemical Society, 2025-09-20) Hamed I. Hamouda; Mohamed H. El-Sayed; Hussein N. Nassar; Nour Sh. El-Gendy; Samah Baleegh Meawad; Wenxia Wang; Tang Li; Heng Yin
Fucose is a key deoxyhexose found in polysaccharides, glycolipids, and glycoproteins. 2′-Fucosyllactose (2′FL), a major human milk oligosaccharide with health benefits for infants, faces production challenges due to the limited availability of efficient α-l-fucosidases. Here, we present a novel α-l-fucosidase, True-Fuc, from Truepera sp. heterologously expressed for 2′FL biosynthesis. True-Fuc, a GH29A family enzyme (50 kDa), showed optimal activity at 50 °C and pH 8.0, hydrolyzing para-nitrophenyl-α-l-fucopyranoside (pNP-α-Fuc), Lewisa, and Lewisxsubstrates. It catalyzed 2′FL synthesis via transfucosylation using pNP-α-Fuc and lactose with minimal degradation of 2′FL and 3-fucosyllactose (3FL). Molecular dynamics simulations revealed that loops 1–4 surrounding the substrate pocket mediate substrate recognition, while the flexible C-terminal loop 5 plays a minor role. These results establish True-Fuc as a promising tool for cost-effective 2′FL production and novel glycoside synthesis. © 2025 American Chemical Society
Evaluating Egyptian citizens’ perception toward introducing voice assistant technology as a means of improving public service delivery: utilizing machine learning as an additional perception’s predictor
(Springer open, 2025-10-04) Yasser Halim; Hazem Halim; Karim Salem; Israa Lewaaelhamd
Purpose: This research assesses the inclination of Egyptian citizens toward embracing Voice Assistant Technology (VAT) to deliver public services based on the functioning of perceived usefulness, ease of use, trust, and perceived risk. This also examines the possibility of using machine learning (ML) models to forecast adoption behavior. Design/methodology/approach: A mixed-method design was applied, supplementing survey data from 398 participants with qualitative analyses of expert interviews. An extended Technology Acceptance Model (TAM) incorporating trustworthiness and perceived risk was employed. Additionally, ten (ML) algorithms were applied to predict acceptance by citizens. Findings: Helpful conclusions were reached, the most helpful being that usefulness, ease of use, and trust highly and positively affect (VAT) acceptance while perceived risk highly and negatively affects VAT acceptance. (ML) analysis validated these findings with Stochastic Gradient Descent (71.9% accuracy) and Ridge Regression (70.9%) as the best predictors, yet Decision Tree was poor (49.3%). These conclusions indicate that risk perceptions need to be addressed and trust enhanced to facilitate VAT adoption in developing-country contexts. Originality/value: This paper contributes to the field by extending TAM with trust and risk factors and adding ML predictive modeling to public administration studies. The results provide policy practitioners and technologists with actionable advice on how to incentivize AI-enabled public service delivery via citizen-focused, trust-building approaches.
Customer Engagement in Digital Health Transformation as Strategic Change: Evidence from Saudi Arabia’s Vision 2030
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-09-21) Abdulrahman Aldogiher; Yasser Tawfik Halim
Purpose: The purpose of this paper is to explore how perceptions of digital health transformation play a role in Saudi Arabia’s customer engagement in healthcare, according to Vision 2030. Saudi Vision 2030, a national reform agenda, has prioritized healthcare digitalization to enhance efficiency, access, and patient-centered care. In particular, the research attempts to explore the attitude of the patient and whether cultural values and infrastructure issues play a mediator role in the perception–engagement relationship. Design/methodology/approach: The study used a mixed-method approach, with qualitative interviews from providers and consumers, along with survey responses from 402 users of digital health. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine hypothesized relationships and moderation effects. Findings: Findings establish that digital health perceptions are a crucial driver in enhancing engagement (β = 0.386; p < 0.001). Perceived ease of use (β = 0.368) and usefulness (β = 0.530) exhibited strong positive influences. Moderation analysis revealed that cultural values (β = 0.343) and infrastructure (β = 0.253) further enhance engagement. The findings highlight usability, usefulness, and context as foundational enablers of long-term patient engagement. Originality/value: By combining Technology Acceptance Model (TAM) variables and applying cultural and infrastructural moderators, this research provides new empirical evidence of Saudi Arabian digital health adoption. It provides policy and practical advice in the creation of accessible, culturally appropriate, and adequately supported digital health solutions toward Vision 2030. It also supports United Nations Sustainable Development Goals (SDGs). The study aligns with SDG 3 (Good Health and Well-Being), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by further promoting sustainable healthcare transformation in a global development agenda.
Design and validation of a low‑cost 3D intraoral scanner using structured‑light triangulation and deep‑learning reconstruction
(Elsevier Inc., 2025-09-26) Ahmed M.M. Awad; Ahmed Badway; Lamiaa ElFadaly
Statement of problem: Intraoral scanners (IOSs) have transformed prosthodontic workflows by enabling precise, high-resolution digital scans. However, their high cost and hardware complexity limit adoption in resource-constrained settings. Purpose: The aim of this study was to design and validate a lightweight, cost-effective IOS prototype hardware using structured-light triangulation and deep-learning reconstruction and to compare its performance with a popular commercially available IOS (TRIOS 3). Material and methods: A handheld prototype IOS hardware integrating a complementary metal-oxide-semiconductor (CMOS) camera (1280×720 px) with both white‑light and red‑laser projectors was developed. Intrinsic and extrinsic calibration used the Zhang method; feature extraction used Canny and scale-invariant feature transform (SIFT), structure‑from‑motion (SfM), and active triangulation generated point clouds in a photogrammetry software program. A YOLO‑V8–style network performed tooth segmentation, followed by a fully convolutional network (FCN) encoder–decoder for depth refinement. A gypsum cast was scanned (307 frames), and the 311 000 initial mesh points outputted were compared against the TRIOS 3 (102 000 points). Results: The mean ±standard deviation reprojection error of the prototype scanner hardware was 0.30 ±0.15 px (range 0.05 to 1.8 px), within commercial tolerances (0.2 to 0.4 px). The landmark count averaged 4000 ±1200 features per frame. After mesh filtering, 270 000 high‑quality vertices remained. Deep‑learning postprocessing reduced surface artifacts by approximately 20% (qualitative). Conclusions: The low‑cost IOS achieved point‑cloud densities 3 times higher than the commercially available IOS while maintaining comparable accuracy, demonstrating its potential in affordable digital prosthetic workflows. Future in vivo validation is planned to determine clinical applicability.
Catalytic pyrolysis of non-textile button components as a co-feedstock for bioenergy production
(Elsevier Ltd, 2026-01-02) Samy Yousef; Justas Eimontas; Nerijus Striūgas; Mohammed Ali Abdelnaby
Recently, the co-pyrolysis of biomass and various types of plastic waste (PW) has shown great potential in improving H/Ceff ratio of biomass, making it a sustainable and competitive source of bioenergy, especially in the presence of catalysts. However, this strategy is limited by high contamination and the difficulty of sorting PW, requiring the development of another clean and uniform source of PW. In this context, this research presents polyester and nylon buttons (major part of non-textile components) as a new type of clean and sortable PW for this purpose. The experiments at this stage was focused on studying the catalytic pyrolysis of plastic buttons only by thermogravimetric analysis (TGA) coupled with Fourier transform infrared (TG-FTIR) and gas chromatography-mass spectrometry (GC/MS) to provide the basic data needed for future co-pyrolysis with biomass. The energy consumed during the reaction (Ea) and other catalytic pyrolysis characteristics over ZSM-5 zeolite catalyst were evaluated using kinetic models along with determination of their thermodynamic parameters. Also, an artificial neural network (ANN) algorithm was proposed to expect TGA properties of buttons at ambiguous heating parameters. The TGA results revealed that polyester sample can be decomposed in two stages up to 360 °C and 460 °C, while nylon sample decomposed in a single stage up to 490 °C. The TGA-FTIR analysis highlighted that carbonyl groups (polyester) and aliphatic hydrocarbons (nylon) are the main functional groups of polyester and nylon vapors. Meanwhile, benzoic acid (72.94 % at 20 min/°C) the main compound of nylon sample and 1,2-Benzenedicarboxylic acid (plasticizers) the main compound of polyester and its toxic styrene compound was completely removed. Finally, the Ea used in decomposition of buttons was estimated at 241.6–262.7 kJ/mol (polyester) and 165.6–173.4 kJ/mol (nylon). The suggested ANN model showed high potential in predicting the catalytic pyrolysis characteristics with R > 0.98. Based on these findings, plastic buttons can be used as a co-feeding hydrogen-rich source to biomass to enhance its H/Ceff ratio and aromatic compounds.