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
LexiSem: A re-ranker balancing lexical and semantic quality for enhanced abstractive summarization
(Elsevier B.V., 2025-07-02) Eman Aloraini; Hozaifa Kassab; Ali Hamdi; Khaled Shaban
Sequence-to-sequence neural networks have recently achieved significant success in abstractive summarization, especially through fine-tuning large pre-trained language models on downstream datasets. However, these models frequently suffer from exposure bias, which can impair their performance. To address this, re-ranking systems have been introduced, but their potential remains underexplored despite some demonstrated performance gains. Most prior work relies on ROUGE scores and aligned candidate summaries for ranking, exposing a substantial gap between semantic similarity and lexical overlap metrics. In this study, we demonstrate that a second-stage model can be trained to re-rank a set of summary candidates, significantly enhancing performance. Our novel approach leverages a re-ranker that balance lexical and semantic quality. Additionally, we introduce a new strategy for defining negative samples in ranking models. Through experiments on the CNN/DailyMail, XSum and Reddit TIFU datasets, we show that our method effectively estimates the semantic content of summaries without compromising lexical quality. In particular, our method sets a new performance benchmark on the CNN/DailyMail dataset (48.18 R1, 24.46 R2, 45.05 RL) and on Reddit TIFU (30.37 R1,RL 23.87).
Zonisamide nanodiamonds for brain targeting: A comprehensive study utilising in silico, in vitro, in vivo, and molecular investigation for successful nose‑to‑brain delivery for epilepsy management
(Springer Publishing Company, 2025-07-05) Nihal Mohamed Elmahdy Elsayyad; Omar A. Elkady; Mohamed M. Swidan; Hassan M. Rashed; Tamer M. Sakr; Amr M. Abdelhamid; Mai A. Zaafan; Hanan M. El‑Laithy
The blood–brain barrier (BBB) is a stringent barrier that restricts the successful brain delivery of polar neurotherapeutics
molecules. One such molecule is Zonisamide (ZNS), a hydrophilic centrally acting anti-epileptic drug. This study aims
to overcome the poor ZNS BBB permeability using the nose-to-brain (NTB) carbon-based biocompatible nanodiamonds
(ND) delivery system to deliver ZNS directly to the brain, bypassing the BBB, thereby enhancing its efcacy and reducing
systemic side efects associated with oral ZNS currently available formulation in clinical practice. Intranasal (IN) ND-ZNS
formulations were optimised using an Artifcial neuronal network (ANN) and assessed for particle size (PS), zeta potential,
loading efciency (%LE), morphology, and in vitro release. The optimum radiolabelled ND-ZNS complex F1 biodistribution in diferent organs and its pharmacokinetics were compared to oral and IN-free ZNS in mice. Temporal lobe epilepsy
(TLE) model in rats was used to compare the anti-epileptic activity of IN ND-ZNS F1 to IN free ZNS by assessing brain
activity, epileptic biomarkers such as (brain neuronal specifc enolase (NSE), neuroflament light polypeptide (NEFL), and
matrix metallopeptidase-9 (MMP-9)), hippocampal histopathology and the modulatory efect on epigenetic miR-199/SIRT-1
and PVT-1/BDNF pathways. Optimized ND-ZNS complex F1 consists of a ZNS:ND ratio of 1:2 and sonicated for 5 min
exhibited the least PS (193.7±19.3 nm), adequate %LE (87.1±9.2%) similar to ANN predictions, with a biphasic in vitro
release profle of ZNS, benefcial for both acute and chronic epilepsy treatment. The IN delivery of ND-ZNS complex F1
showed preferential higher in vivo brain uptake with minimal systemic exposure linked with higher brain/blood ratio and
signifcant (p≤0.05) overall enhanced pharmacokinetics expressed by Cmax and AUC (0-120min) when compared to oral and IN
free ZNS.
Moreover, the TLE model confrmed the improved anti-epileptic activity of F1 compared to IN-free ZNS regarding brain activity and hippocampal histopathology, signifcant suppression of serum NSE, NEFL, MMP-9 levels, miR-199/
SIRT-1 pathway, and normalization of PVT-1/BDNF pathway. Therefore, ND used in this study could be a novel, promising
carrier to target ZNS directly to the brain via the IN route for efective epilepsy management with less drug dosing and the
least systemic side efects.
Probing the exchange rate’s asymmetric reaction to oil price changes in the new BRICS Plus group
(Non-profit partnership 'Voprosy Ekonomiki', 2025-06-30) Heba Helmy
We aim to show how any variants of a unified currency among BRICS Plus countries are
challengeable, by probing the disparate influence of the positive and negative alterations
in the crude oil’s international price on the real effective exchange rates. The paper applies
the nonlinear autoregressive distributed lag approach to separate oil price upswings from
downswings and assesses how such changes asymmetrically affect the real effective exchange rates of BRICS Plus members in the short and long runs using monthly time series
variables from January 2000 until July 2023. Our findings reveal that in the short run,
the asymmetric impacts of the positive and negative oil price changes on the real effective
exchange rates appear in all BRICS Plus countries. In the long run, Brazil and Argentina
confirmed the asymmetric impact of oil price changes on their real effective exchange
rates, while the symmetric impact is confirmed in Russia, the United Arab Emirates, and
Ethiopia. Our findings prove that a unified currency or a unified monetary union is a very
challenging idea, as continuous appreciations or depreciations of the local currencies
of BRICS Plus countries will have to be implemented to preserve their alignment with
the composite currency unit. Moreover, the asymmetric responses will induce diverse
policy recommendations concerning the oil pricing. Our study comes to fill a vital lacuna
in the literature as it is the first study to probe the asymmetric association between the oil’s
international price and the real effective exchange rate in the BRICS Plus countries.
CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning
(Nature Research, 2025-06-02) Afnan Al-Ali; Ali Hamdi; Mohamed Elshrif; Keivin Isufaj; Khaled Shaban; Peter Chauvin; Sreenath Madathil; Ammar Daer; FalehTamimi; Raidan Ba-Hattab
Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for
early detection of oral lesions. This study presents a novel deep learning framework for multi-class
classification-based segmentation, enabling accurate differential diagnosis of 14 common oral
lesions—benign, pre-malignant, and malignant—across various mouth locations using photographic
images. A dataset of 2,072 clinical images was used to train and validate the model. The proposed
framework integrates EfficientNet-B3 for classification and ResNet-101-based Mask R-CNN for
segmentation, achieving a classification accuracy of 74.49% and segmentation performance with
an average precision (AP50) of 72.18. The gradient-weighted class activation map technique was
applied to the model outputs to enable visualization of the specific areas that were most influential
for predictive decisions made by the model. This significantly improves upon the state-of-the-art,
where previous models achieved lower segmentation accuracy (AP50<50%). The framework not only
classifies the lesion type but also delineates the lesion boundaries with high precision, which is critical
for early detection and differential diagnosis in clinical practice.
Optimizing Resilient Sustainable Citrus Supply Chain Design
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-06-03) Sherin Bishara; Nermine Harraz; Hamdy Elwany; Hadi Fors
Background: Growing environmental concerns and the vulnerability of global
supply chains to disruptions, such as pandemics, natural disasters, and logistical failures, necessitate the design of sustainable and resilient supply chains. Methods: A novel
multi-period mixed-integer linear programming model is developed with the objective of
maximizing supply chain profit to design a complete citrus supply chain, which incorporates the production of citrus fruit and juice, and accommodates resilience and sustainability
perspectives. Results: A comprehensive citrus supply chain scenario is presented to support
the applicability of the proposed model, leveraging real data from citrus supply chain stakeholders in Egypt. Moreover, an actual case study involving a citrus processing company in
Egypt is demonstrated. Gurobi software is used to solve the developed model. To build a
resilient supply chain which can cope with different disruptions, different scenarios are
modeled and strategies for having multiple suppliers, backup capacity, and alternative
logistics routes are evaluated. Conclusions: The findings underscore the critical role of
resilience in supply chain management, particularly in the agri-food sector. Moreover, the
proposed model not only maximizes supply chain profitability but also equips stakeholders
with the tools necessary to navigate challenges effectively.