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
LLM-DaaS: LLM-driven Drone-as-a-Service Operations from Text User Requests
(Springer Science and Business Media Deutschland GmbH, 2025-06-26) Lillian Wassim; Kamal Mohamed; Ali Hamdi
We propose LLM-DaaS, a novel Drone-as-a-Service (DaaS)
framework that leverages Large Language Models (LLMs) to transform
free-text user requests into structured, actionable DaaS operation tasks.
Our approach addresses the key challenge of interpreting and structuring
natural language input to automate drone service operations under uncertain conditions. The system is composed of three main components:
free-text request processing, structured request generation, and dynamic
DaaS selection and composition. First, we fine-tune different LLM models
such as Phi-3.5, LLaMA-3.2 7b and Gemma 2b on a dataset of text user
requests mapped to structured DaaS requests. Users interact with our
model in a free conversational style, discussing package delivery requests,
while the fine-tuned LLM extracts DaaS metadata such as delivery time,
source and destination locations, and package weight. The DaaS service
selection model is designed to select the best available drone capable of
delivering the requested package from the delivery point to the nearest
optimal destination. Additionally, the DaaS composition model composes
a service from a set of the best available drones to deliver the package
from the source to the final destination. Second, the system integrates
real-time weather data to optimize drone route planning and scheduling, ensuring safe and efficient operations. Simulations demonstrate the
system’s ability to significantly improve task accuracy, operational efficiency, and establish LLM-DaaS as a robust solution for DaaS operations in uncertain environments.
Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models
(Springer Science and Business Media Deutschland GmbH, 2025-06-26) Mohamed Basem; Islam Oshallah; Baraa Hikal; Ali Hamdi; Ammar Mohamed
Understanding the deep meanings of the Qur’an and the
bridge the language gap between modern standard Arabic and classical Arabic is essential to improve the question-and-answer system for
the Holy Qur’an. The Qur’an QA 2023 shared task dataset had limited
number of questions with weak model retrieval. To address this challenge, this work was done to update the original dataset and improve
the model accuracy. The original dataset which contains 251 questions
was reviewed and expanded to 629 questions with questions diversification and reformulation, leading to a comprehensive set of 1895 categorized into single-answer, multi-answer, and zero-answer types. Extensive experiments fine-tuned transformer models, including AraBERT,
RoBERTa, CAMeLBERT, AraELECTRA, and BERT. The paper best
model, AraBERT-base, achieved a MAP@10 of 0.36 and MRR of
0.59, representing improvements of 63% and 59%, respectively, compared
to the baseline scores (MAP@10: 0.22, MRR: 0.37). Additionally, the
dataset expansion led to improvements in handling” no answer” cases,
with the proposed approach achieving a 75% success rate for such
instances, compared to the baseline’s 25%. These results demonstrate
the effect of dataset improvement and model architecture optimization in
increasing the performance of QA systems for Holy Qur’an, with higher
accuracy, recall, and precision.
Enhanced LED light driven photocatalytic degradation of Cefdinir using bismuth titanate nanoparticles
(Nature Research, 2025-07-08) Sara Ishaq; Ahmed H. Nadim; Joliana F. Farid; Sawsan M.Amer; Heba T. Elbalkiny
Photodegradation of antibiotics using visible light represents a promising approach for efficiently
removing antibiotic contaminants from water sources. This study investigated bismuth titanate
(Bi4Ti3O12) nanoparticles for the photodegradation of Cefdinir (CEF), a third-generation
cephalosporin, under visible LED irradiation. Bismuth titanate nanoparticles were synthesized and
characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), and diffuse
reflectance spectroscopy (DRS). Factors affecting the degradation protocol were optimized using a
central composite design model, and the degradation efficiency was assessed using a validated RPHPLC method. Results of the experimental design demonstrated that bismuth titanate nanoparticles
exhibited high photocatalytic performance (⁓ 98% photodegradation), which was found in an
optimum condition of 0.05 g/L of BIT-NP in pH 5 for 50 µg/mL of CEF in 1 h at room temperature. The
degradation efficiency depended on the concentration of the nanoparticles, the initial concentration
of CEF, and pH. The antimicrobial effect of CEF was assessed before and after the degradation process,
and the loss of antibiotic activity was observed after treatment. The findings provide valuable insights
into developing innovative photocatalytic materials for the economic remediation of antibioticcontaminated water sources using eco-friendly LED sources for degradation under visible light for the
first time. This would offer a promising solution to mitigate the environmental impact of antibiotic
residues.
Nexus among herding behavior, ESG disclosure, and market capitalization in the Egyptian stock market
(Springer open, 2025-07-09) Mohamed Samy ElDeeb; Nada Salah ElGabry; Nesma Mounir; Mirna Ahmed
Purpose This study aims to investigate the impact of herding behavior on environmental, social, governance (ESG)
disclosure among frms listed on the Egyptian stock exchange, with the moderating role of market capitalization.
Design/methodology/approach The sample consists of 37 Egyptian frms within the EGX70 index covering
the period between 2019 and 2023. The analysis employs a panel data analysis using a Fixed Efects Model. ESG
disclosure was measured using the ESG index, while herding behavior was measured by stock return dispersion,
and market capitalization was measured by multiplying the number of shares outstanding by price.
Research Limitation/Implication This study is limited to the context of Egypt and the data sample, with further
limitations including a small sample size and the infuence of COVID-19 during the time period used. However, this
study contributes to the growing literature on ESG and investigates the behavioral drivers of disclosure specifcally
in the Egyptian stock market.
Findings Results reveal that herding behavior has a positive but insignifcant relationship with ESG disclosure. The
study further investigates the moderating role of market capitalization, which has a positive but insignifcant relationship, and has two control variables, with frm size showing a signifcant positive relationship, while fnancial leverage
shows an insignifcant positive relationship with the ESG disclosure. Overall, while some frms may imitate other frms
in disclosing ESG practices, herding behavior is not the dominant force for ESG disclosure in Egypt.
Theoretical implications The study combined stakeholder theory and behavioral fnance to provide evidence
that internal and regulatory mechanisms overwhelm herding forces in emerging economies. The research resists typical market imitation thought by placing emphasis on institutional structure and frm-specifc resources.
Originality/value This study has valuable insights for investors, regulators, and corporate directors by highlighting
the limited impact of market imitation and emphasizing the importance of internal company characteristics in ESG
disclosure. Moreover, the study suggests that a more robust regulatory framework must be in place and that more
awareness initiatives should be implemented to promote better ESG disclosure across the Egyptian fnancial market.
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).