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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Recent Submissions
Pyrolysis behavior of non-textile components (buttons) and their kinetic analysis using artificial neural network
(Elsevier B.V, 2024-11-26) Samy Yousef; Justas Eimontas; Nerijus Striūgas; Marius Praspaliauskas; Mohammed Ali Abdelnaby
This research aims to study the pyrolysis behavior of old buttons (main non-textile components) and their kinetic behavior to convert them into energy and their original chemical compounds. The pyrolysis experiments were performed using a thermogravimetric analyzer (TG) on buttons have different composition that were defined using FTIR, elemental and proximate analysis. The composition of the valuable chemicals generated from the pyrolysis process were observed TG-FTIR and GC/MS. The kinetic parameters of the decomposition process were also studied using conventional modeling methods and artificial neural network (ANN) as an advanced machine learning tool. The results showed that polyester, nylon and their blends are the most commonly used materials in button manufacturing. The physical analysis showed that the buttons are very rich in volatile matter content (92.08–99.67 wt%) and completely decompose up to 490 °C at 92–100 wt%. Meanwhile, GC/MS showed that the pyrolysis vapors released from polyester buttons were rich in styrene (84.54 %), while caprolactam (40.30 %) was the dominant compound in nylon buttons versus naphthalene, 1,2,3,4-tetrahydro-2-phenyl- (67.71 %) was the major compound in the mixture sample. The kinetic analysis showed that the activation energy of the degradation process was in the ranges of 152–202 kJ/mol (polyester), 156–201 kJ/mol (nylon), 402–449 kJ/mol (mixed) and the ANN model was successfully trained and predicted the degradation regions of the buttons. Accordingly, pyrolysis of buttons is highly recommended to valorize buttons and convert them into parent chemical compounds.
Laser‑assisted Icon and clinpro for restoring white spot lesions: an in vitro comparative study
(Springer Japan, 2024-11-26) Yomna Said Mohamed; Mohamed Shamel; Sara El Banna
Managing white spot lesions (WSLs) remains a challenging issue that has yet to be fully resolved. WSLs are areas of demineralized enamel that most commonly occur following orthodontic treatments. They can potentially lead to enamel caries and
are also esthetically undesirable. The current study investigated and analyzed the efects of Icon resin infltration (Icon) and
Clinpro XT varnish (Clinpro), both alone and in combination with a diode laser, on the restoration of WSLs. Color change,
microhardness, and scanning electron microscopy were used to evaluate the WSLs after the diferent treatment applications.
Results showed that the combination of Icon and Clinpro, along with a diode laser, enhanced color stability and restoration
of enamel hardness in white spot lesions. Utilizing a diode laser signifcantly improved the efcacy of both Icon and Clinpro
therapies. SEM examination verifed that laser-assisted treatments resulted in almost total blockage of enamel rods, indicating
enhanced efectiveness. Conclusions: Integrating diode laser treatment with Icon and Clinpro XT Varnish has signifcantly
improved the esthetic outcomes and mechanical properties of treated enamel.
Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques
(Nature Publishing Group, 2024-11-04) Ahmed Samir; Amira Abdeldaim; Ammar Mohammed; Asmaa Ali; Mohamed Alorabi; Mariam M. Hussein; Yasser Mabrouk Bakr; Asmaa Mohamed Ibrahim; Ahmed Samir Abdelhafiz
Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable
to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge
due to the lack of widespread screening among at-risk populations. The objective of this study was
to assess the utility of machine learning in predicting HCC by analyzing the combined expression
of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152,
LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched
controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis.
Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to
integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual
lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60
to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior
performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5
expression ratio significantly correlated with increased mortality risk. The integration of lncRNA
biomarkers with conventional laboratory data within a machine learning framework demonstrates
significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the
model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and
explore a wider array of lncRNAs.
A Compact Dual-Band ACS-Fed Frequency Independent Hook Loop Antenna for WLAN Applications
(Taylor and Francis Ltd., 2024-10-02) Arvind Kumar; Praveen V. Naidu; Mohamed El Atrash; Vinay Kumar; Mahmoud A. Abdalla
Presented in this paper is a simple, small-sized, and hook-shaped monopole antenna fed by an Asymmetric Coplanar Strip (ACS) for different dual-band operations. The antenna operates at 3.5 and 5.2 GHz; hence, serving WiMAX and WLAN applications. Dual resonances were attained by integrating a quarter-wavelength strip with a C-shaped half-wavelength loop strip. The proposed antenna footprint is 20 × 7.9 mm2. Based on the achieved outcomes and the antenna compactness, it can be highly nominated for use in 3.5/5 GHz wireless communications applications and wireless gadgets.
Fusing CNNs and attention-mechanisms to improve real-time indoor Human Activity Recognition for classifying home-based physical rehabilitation exercises
(Elsevier Ltd, 2025-01-01) Moamen Zaher; Amr S. Ghoneim; Laila Abdelhamid; Ayman Atia
Physical rehabilitation plays a critical role in enhancing health outcomes globally. However, the shortage of
physiotherapists, particularly in developing countries where the ratio is approximately ten physiotherapists
per million people, poses a significant challenge to effective rehabilitation services. The existing literature
on rehabilitation often falls short in data representation and the employment of diverse modalities, limiting
the potential for advanced therapeutic interventions. To address this gap, This study integrates Computer
Vision and Human Activity Recognition (HAR) technologies to support home-based rehabilitation. The study
mitigates this gap by exploring various modalities and proposing a framework for data representation. We
introduce a novel framework that leverages both Continuous Wavelet Transform (CWT) and Mel-Frequency
Cepstral Coefficients (MFCC) for skeletal data representation. CWT is particularly valuable for capturing
the time-frequency characteristics of dynamic movements involved in rehabilitation exercises, enabling a
comprehensive depiction of both temporal and spectral features. This dual capability is crucial for accurately
modelling the complex and variable nature of rehabilitation exercises. In our analysis, we evaluate 20 CNNbased models and one Vision Transformer (ViT) model. Additionally, we propose 12 hybrid architectures
that combine CNN-based models with ViT in bi-model and tri-model configurations. These models are
rigorously tested on the UI-PRMD and KIMORE benchmark datasets using key evaluation metrics, including
accuracy, precision, recall, and F1-score, with 5-fold cross-validation. Our evaluation also considers realtime performance, model size, and efficiency on low-power devices, emphasising practical applicability.
The proposed fused tri-model architectures outperform both single-architectures and bi-model configurations,
demonstrating robust performance across both datasets and making the fused models the preferred choice for
rehabilitation tasks. Our proposed hybrid model, DenMobVit, consistently surpasses state-of-the-art methods,
achieving accuracy improvements of 2.9% and 1.97% on the UI-PRMD and KIMORE datasets, respectively.
These findings highlight the effectiveness of our approach in advancing rehabilitation technologies and bridging
the gap in physiotherapy services.