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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MSA Journals 140
A Full content for MSA university Faculties Journals
MSA Theses 5
A digital collection of MSA University postgraduate theses, including PhD and Master’s theses, organized by academic degree and faculty.
MSA University Academic Graduation Projects 1153
A Full content for msa university Distinguished Graduation Projects Yearbook
MSA University Academic Research 3748
MSA University Campus 2
Images for MSA University " sites - building - landscape "

Recent Submissions

An innovative approach for predicting default risk in peer-to-peer lending using stacking ensemble models with explainable machine learning
(Springer London, 2026-06-16) Markus Atef; Menna Ibrahim Gabr; Wafaa Seoud; Shimaa Ouf
Peer-to-peer (P2P) lending has increased significantly during the past few years on a global scale. However, there are several challenges associated with P2P lending’s rapid rise. The major challenges are imbalanced datasets, which make machine learning difficult, an excessive number of features, and low-performing classification algorithms. Furthermore, machine learning models face another complex challenge referred to as the black-box problem. To address these challenges, an innovative approach was developed by first applying Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in the Bondora dataset, followed by the implementation of multiple feature selection techniques: Chi-Square (filter), Sequential Backward Selection (SBS) (wrapper), and embedded methods such as Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). A range of classifiers, linear (Logistic Regression (LR)), non-linear (Support Vector Machine (SVM), Naive Bayes (NB), and tree-based models (Decision Tree (DT), RF, Adaptive Boosting (AdaBoost), CatBoost), were then used to predict loan defaults. The top-performing models were integrated into various stacking ensembles using GBM, Extreme Gradient Boosting (XGBoost), and LightGBM as meta-learners to enhance predictive accuracy. The results declared that LightGBM exhibited an outstanding performance with accuracy, F-score, and Area Under the Curve (AUC) values of 0.981, 0.980, and 0.994, respectively, showing better performance than that reported in the literature. Explainable models were employed to interpret predictions and enhance user trust. Specifically, the LightGBM stacking model was combined with the Local Interpretable Model-agnostic Explanations (LIME) framework to provide interpretable insights into its prediction results.
Nanoparticle-based delivery of corn silk extract for the treatment of CCl₄-induced hepatotoxicity
(Elsevier B.V., 2026-06-08) Alyaa Farid; Aya Badawi; Malak Waheed; Mohamed Ashraf; Maryam Amr; Ayman Amin
Liver injury is a significant global health concern that requires effective therapeutic strategies. Corn silk is traditionally recognized for its health-promoting properties due to its rich content of bioactive compounds. This study aimed to develop corn silk extract-loaded chitosan nanoparticles (CSE-CSNPs) via ionic gelation and evaluate their hepatoprotective efficacy against CCl₄-induced liver injury in rats. CSE-CSNPs exhibited favorable physicochemical properties, including a mean size of 59.03 nm and a positive zeta potential of 45.61 mV, with encapsulation efficiency of 73.2% for total phenolics. In vitro assays confirmed the biocompatibility and enhanced antioxidant activity of the nanoformulation. In vivo, treatment with CSE-CSNPs (100 mg/kg/day) for four weeks significantly ameliorated CCl₄-induced liver injury. Serum ALT levels decreased from 84.2 U/L in untreated CCl₄ rats to 49.2 U/L in CSE-CSNPs-treated rats, approaching the control value of 50.6 U/L. Similarly, AST and ALP were reduced from 101.0 U/L and 149.6 U/L to 70.6 U/L and 109.6 U/L, respectively. Oxidative stress markers showed comparable restoration, with hepatic MDA decreasing from 24.0 to 10.6 ng/mg protein, while GSH, SOD, and CAT increased from 55.4 μg/mg protein, 31.2 U/mg protein, and 22.2 U/mg protein to 97.6 μg/mg protein, 55.2 U/mg protein, and 63.4 U/mg protein, respectively- all values comparable to healthy controls. Notably, the nanoformulation achieved superior hepatoprotection at half the dose of free corn silk extract, demonstrating enhanced bioavailability and therapeutic efficacy. These findings highlight the potential of chitosan nanoparticle-mediated delivery as a promising strategy to improve the hepatoprotective activity of corn silk extract.
Nanomaterial-enhanced voltammetric sensor for concurrent monitoring of aprepitant and apixaban in plasma toward precision medicine and in pharmaceutical analysis
(Elsevier Inc., 2026-06-11) Rasha Th. El-Eryan; Mona S. Elshahed; Dalia Mohamed; Azza A. Ashour; Heba T. Elbalkiny
The growing demand for precision medicine has driven the development of nanomaterial-modified electrodes for applications in this field. This work reports novel voltammetric platforms based on functionalized carbon paste electrodes (CPEs) with engineered nanomaterials for the concurrent determination of aprepitant (APR) and apixaban (APX) in human plasma, as well as the first voltammetric assay for APR in pharmaceutical capsules. Monitoring both drugs is clinically relevant because APR may inhibit APX metabolism, increasing bleeding risk during coadministration. For simultaneous plasma analysis, a CPE was modified with 2% zinc oxide nanoparticles (ZnO NPs) and 1% carbon dots (CDs) synthesized from pepper seeds, a sustainable green precursor. This nanocomposite-modified sensor achieved linear ranges of 0.28–1.31 μM (0.15–0.70 μg/mL) for APR and 0.02–1.09 μM (0.01–0.50 μg/mL) for APX. For individual determination of APR in capsules, a CPE incorporating 1% multi-walled carbon nanotubes (MWCNTs) and 1% CDs provided a linear range of 0.19–2.06 μM (0.10–1.10 μg/mL). All proposed methods were validated according to international guidelines. Furthermore, the greenness of the analytical procedures was systematically evaluated, confirming their environmental compatibility and alignment with sustainable chemistry principles. The developed sensors offer a promising step toward a precision medicine strategy for therapeutic drug monitoring and risk assessment of drug-drug interactions, with potential for integration into portable diagnostic devices. In addition, they demonstrate valuable applicability in pharmaceutical quality control laboratories.
Enhanced load frequency control using a novel fractional-order integral-integral-derivative controller optimized by cuckoo catfish optimizer
(Nature Research, 2026-06-11) Mohamed Barakat; Ahmed Donkol; Mohammed Sekhi; A. M. Mabrouk
Maintaining frequency stability in interconnected power systems (IPSs) is a critical challenge, particularly under sudden load changes and nonlinear constraints. Conventional PID and fractional-order controllers (FOPID, TID, FOID, and cascaded FO-PID structures) either lack adaptability or introduce excessive complexity. To overcome these limitations, this study introduces a novel fractional-order integral–integral–derivative (FOIID) controller that replaces the proportional term with a second-order fractional integrator. This dual-integral design yields 27 possible configurations, enhances the low-frequency gain, and eliminates the steady-state ramp error, thereby improving the transient and steady-state performance. The recently developed cuckoo catfish optimizer (CCO) is employed for parameter tuning. Unlike conventional metaheuristics (PSO, GA, and GWO), CCO integrates cooperative space compression, chaotic predation, and adaptive regeneration strategies, which avoid premature convergence and achieve a robust global search. The proposed CCO–FOIID framework was validated on a two-area non-reheat benchmark system and further tested on a three-area thermal–thermal–hydro system with generation rate constraints under 1% and 2% step load disturbances. A comparative analysis against five state-of-the-art controllers (ISFS–PID, DSA–FOPID, WHO–PI(1 + FOPID), and CGO–FOPID–FOPI) demonstrates that CCO–FOIID consistently achieves faster settling times, reduced overshoot, and the lowest ITAE value (74.26), outperforming the best competitor (CGO–FOPID–FOPI, 82.67). These results confirm that the combination of FOIID’s universal structure and CCO’s robust optimization provides a simple yet powerful solution for modern LFC applications in both simple and complex IPS networks.
Global Perspective on the Evolution and Future of Pharmacovigilance: Deliverables from the 24th Annual Meeting of the International Society of Pharmacovigilance Celebrating 25 Years of Excellence
(Springer Nature, 2026-06-02) Mohamed A. Elhawary; Brian Edwards; Hadir Rostom; Shanthi N. Pal; Viola Macolic Sarinic; Priya Bahri; Deirdre McCarthy; Victoria Prudence Nambasa; Linda Härmark; Andrew Bate; Rebecca Chandler; Marco Tuccori; Fawaz Alharbi; Mohammed Fouda; Comfort Ogar; Jürgen Beckmann; Jayesh Pandit; Mayada Alkhakany; Christina Saad; Manal M. Younus; Ivor Ralph Edwards; Omar Aimer