Research Title: توظيف الذكاء الاصطناعي في تعليم اللغة والنحو لطلبة أقسام اللغة العربية: دراسة ميدانية لتأثير التقنيات الحديثة على تطوير المهارات اللغوية

Author: Omar Hajeej Alrawajfeh, Published Year: 2025

Faculty: Arts

Abstract: ملخص الأهداف: يهدف هذا البحث إلى استقصاء دور الذكاء الاصطناعي في تطوير تعليم طلبة أقسام اللغة العربية في الجامعات، مع التركيز على تحليل الفوائد والتحديات المترتبة على استخدام هذه التقنيات. كما يستهدف البحث الوقوف على مدى تأثير الذكاء الاصطناعي في تعزيز كفاءة الطلاب، وتحسين جودة تعلمهم، بالإضافة إلى ذلك تحقيق الأهداف التعليمية بشكل أسرع وأكثر دقة، أيضا إلى استكشاف الفجوة بين الواقع الحالي لإدماج الذكاء الاصطناعي في التعليم والفرص المستقبلية. المنهجية: اعتمد البحث على استبيان ميداني مُصمم بعناية طبق على عينة شملت 216 طالبًا وطالبة من قسم اللغة العربية في جامعة فيلادلفيا في الأردن. وقد تنوعت أسئلة الاستبيان لتغطي الجوانب العامة لاستخدام الذكاء الاصطناعي، مثل زيادة المعرفة، وتحسين الأداء الأكاديمي، وحل الواجبات، بالإضافة إلى قياس تصورات الطلاب حول تأثير الذكاء الاصطناعي في الكفاءة والقدرة على التعلم. النتائج: أظهرت النتائج أن الذكاء الاصطناعي يمثل إضافة نوعية للتعليم الجامعي، حيث رأى غالبية الطلاب أنه يسهم في توفير الوقت والجهد وتحسين جودة الأداء الأكاديمي. ومع ذلك، أشارت النتائج إلى تحديات متعلقة بجودة المخرجات باللغة العربية، وضعف التدريب على استخدام التقنيات، كذلك الاعتماد المفرط على الذكاء الاصطناعي مما قد يؤثر سلبًا على تطوير مهارات التفكير النقدي والإبداعي. الخلاصة: يعكس البحث أهمية الذكاء الاصطناعي بوصفه وسيلة تعليمية واعدة، لكنه يبرز الحاجة إلى تطوير أدوات وتقنيات أكثر توافقًا مع خصوصيات اللغة العربية، إضافةً إلى ضرورة تعزيز الوعي الأكاديمي حول الاستخدام الأمثل لهذه الأدوات، بما يضمن تحسين جودة التعليم ويقلل من المخاطر المرتبطة باستخدامها.

Keywords: التعليم الإلكتروني، اللغة العربية، الذكاء الاصطناعي، اللسانيات الحاسوبية

Research Title: The Role of Fintech and Financial inclusion in the economic development of countries: A comparative analysis

Author: Izzeddien Naef Ananzeh, Published Year: 2025

Bank and Banks, 20

Faculty: Business

Abstract: The integration of digital financial technology has revolutionized the global financial system, driving financial inclusion as an important pillar of sustainable economic development. This study examines the multidimensional effects of Digital Financial Technology and Financial Inclusion for Economic Development in middle- and highincome countries. The study employs various indicators of financial inclusion and technology, namely access to the internet, Automated Teller Machines (ATMs), bank branches, and the number of depositors using panel regression analysis covering 20 middle-income countries and 22 high-income countries from 2010 to 2021. The regression analysis results show that ATMs, internet access, bank branches, and number of depositors all have a positive correlation with the Index of Human Development, which was used to measure economic development. This supports the idea that wider use of technology and increased financial inclusion can lead to higher levels of human development. Conversely, the study highlights a negative correlation between inflation rates (as a control variable) and the Human Development Index (HDI) emphasized the significance of maintaining price stability for sustained economic progress. The study concludes that digital financial technology and financial inclusion positively impact the economic development of countries and the disparity between middle- and high-income countries. So, the middle-income countries should prioritize t

Keywords: financial technology, financial inclusion, economic development, panel regression analysis, middle-income countries, high-income countries

Research Title: Anticancer and Cyclooxygenase Inhibitory Activity of Benzylidene Derivatives of Fenobam and its Thio Analogues

Author: Wafa Moh'd Khair Hourani, Published Year: 2025

Faculty: Pharmacy

Abstract: Abstract: Introduction: A series of benzylidene derivatives of fenobam and its thio analogues (1-22) have been evaluated for their cytotoxicity against breast cancer (MCF-7, MDA-MB-231), ovarian cancer (A2780, SKOV-3) and cervical cancer (HELA) cell lines. Method: These compounds (1-22) exhibited 72-83% inhibition of Erk activity against the ovarian cancer cell line (A2780). Compounds 3 and 20 showed the highest DNA damage effect in comet assay against the A2780 cancer cell line as compared to the other tested analogues (4, 8, 11, 12, and 13) by using % Tail DNA and OTM. Compounds 3, 4, and 11 showed significant activities and selectivity towards COX-2 with 78%, 97%, and 89% inhibition, as compared to 17%, 57%, and 26% inhibition against COX-1 isoenzyme, respectively. Results: Interestingly, molecular docking scores were also in very good agreement with the experimental results regarding discriminating the selectivity index of the tested compounds against COX-1 & COX-2 enzymes. Further molecular dynamics (MD) simulation study revealed that the most selective compound, 13, binds with the COX-2 enzyme in a similar fashion to that of Rofecoxib, which was further supported by their MD-based free binding energies (MM-GBSA) of -49.76 ± 4.27 kcal/mol, and -44.84 ±3.78 kcal/mol, respectively. Conclusion: Moreover, in silico ADMET predictions showed adequate properties for these compounds, making them promising leads worthy of further optimization.

Keywords: cancer

Research Title: Unveiling the anti-cancer potentiality of phthalimide-based Analogues targeting tubulin polymerization in MCF-7 cancerous Cells: Rational design, chemical Synthesis, and Biological-coupled Computational investigation

Author: Wafa Moh'd Khair Hourani, Published Year: 2024

Faculty: Pharmacy

Abstract: The present study deals with an anti-cancer investigation of an array of phthalimide-1,2,3-triazole molecular conjugates with various sulfonamide fragments against human breast MCF-7 and prostate PC3 cancer cell lines. The targeted 1,2,3-triazole derivatives 4a-l and 6a-c were synthesized from focused phthalimide-based alkyne precursors using a facile click synthesis approach and were thoroughly characterized using several spectroscopic techniques (IR, 1H, 13C NMR, and elemental analysis). The hybrid click adducts 4b, 4 h, and 6c displayed cytotoxic potency (IC50 values of 1.49, 1.07, and 0.56 μM, respectively) against MCF-7 cells. On the contrary, none of the synthesized compounds showed apparent cytotoxic efficacy for PC3 cells (IC50 ranging from 9.87- >100 μM). As a part of the mechanism analysis, compound 6c demonstrated a potent inhibitory effect (78.3 % inhibition) of tubulin polymerization in vitro with an IC50 value of 6.53 μM. In addition, biological assays showed that compound 6c could prompt apoptotic cell death and induce G2/M cell cycle arrest in MCF-7 cells. Accordingly, compound 6c can be further developed as an anti-breast cancer agent through apoptosis-induction.

Keywords: Keywords: 1,2,3-Triazoles Phthalimides Sulfonamides Apoptosis activity Tubulin inhibitor

Research Title: Design and synthesis of chromene-1,2,3-triazole benzene sulfonamide hybrids as potent carbonic anhydrase-IX inhibitors against prostate cancer

Author: Wafa Moh'd Khair Hourani, Published Year: 2024

Faculty: Pharmacy

Abstract: Considering the promising effects of molecular hybridization on drug discovery in recent years and the ongoing endeavors to develop bioactive scaffolds tethering the 1,2,3-triazole core, the present study sought to investigate whether the 1,2,3-triazole-linked chromene and benzene sulfonamide nucleus could exhibit activity against the human breast cancer cell line MCF-7 and prostate cancer cell line PC-3. To this end, three focused bioactive series of mono- and -bis-1,2,3-triazoles were effectively synthesized via copper-assisted cycloaddition of mono- and/or di-alkyne chromenone derivatives 2a and b and 9 with several sulfa drug azides 4a–d and 6. The resulting molecular derivatives were tested for cytotoxicity against prostate and breast cancer cells. Among the derivatives, 10a, 10c, and 10e exhibited potent cytotoxicity against PC-3 cells with IC50 values of 2.08, 7.57, and 5.52 μM compared to doxorubicin (IC50 = 2.31 μM) with potent inhibition of CA IX with IC50 values of 0.113, 0.134, and 0.214 μM. The most active compound, 10a, was tested for apoptosis-induction; it induced apoptosis by 31.9-fold cell cycle arrest at the G1-phase. Further, the molecular modeling approach highlighted the relevant binding affinity for the top-active compound 10a against CA IX as one of the most prominent PC-3 prostate cancer-associated biotargets

Keywords: cancer

Research Title: Synthesis, Characterization and antitumor activity of ethyl 8-nitrociprofloxacin – 1,2,3-triazole conjugates against prostate cancer cell lines PC3, DU145

Author: Wafa Moh'd Khair Hourani, Published Year: 2024

Faculty: Pharmacy

Abstract: Five novel 8-nitrociprofloxacin-1,2,3-triazole conjugates(9a-e) were synthesized via 1,3-dipolar cycloaddition reaction, by reacting ethyl 1-cyclopropyl-6-fluoro-8-nitro-4-oxo-7-(4-(3-oxobutanoyl) piperazin-1-yl)-1,4-dihydroquinoline- 3-carboxylate(7) with various aryl azides. The new compounds were characterized using High- Resolution Mass Spectrometry(HRMS), 1H NMR, and 13C NMR. Conjugates(9a-e) were tested for their in vitro anticancer activity against two prostate cancer cell lines, namely, PC3, DU145. Conjugates 9d and 9e exhibited remarkable anti-proliferative activity against DU145 and PC3 cell-lines. The IC50 of 9d and 9e for PC3 cell line is 0.0496 ± 0.2372 μM and 0.145 ± 0.337 μM, respectively. All derivatives (9a-e) significantly increased the amount of DNA damage. Two conjugates 9d and 9e, showed enhanced cytotoxic activity against both prostate cancer cell lines in comparison to the other conjugates. Derivatives 9e, 9c and 9d significantly enhanced the expression of p53, Caspases3 and p21 by 4-folds. The AnnexinV–FITC/PI test revealed a late apoptosis at the level of 9.1–13.5 % as performed with DU145 cells cultured with derivatives 9e and 9d

Keywords: Prostate cancer Anti-tumor Ethyl 8-nitrociprofloxacin-1 2, 3-Triazole conjugates PC3 and DU145 Prostate cancer cell lines

Research Title: Artificial intelligence applications for enhancing organizational excellence: Modifying role of supply chain agility”

Author: Shadi Mohammad Al-Tahat, Published Year: 2024

Problems and Perspectives in Management,, 22

Faculty: Business

Abstract: The study’s goal was to demonstrate the modifying role of supply chain agility in the impact of artificial intelligence applications on organizational excellence in Jordanian e-commerce companies. The analytical and descriptive approach was adopted. The study population consisted of 160 companies operating in the e-commerce sector in Jordan. The sample comprised 400 respondents working in senior and middle management positions. The questionnaire was utilized to collect the data. The results showed an impact of artificial intelligence applications in all dimensions (expert systems and neural networks) on the organizational excellence of e-commerce companies in Jordan. The value of the adjusted coefficient of determination (Adj. R2) was .265%. Based on the model’s F value (4.1190) and its level of significance (P; 0.00), the impact of these techniques on organizational excellence is statistically significant. Additionally, the results displayed an impact of supply chain agility on improving the impact of artificial intelligence applications on organizational excellence. The value of the degree of influence ß after introducing the modified variable supply chain agility and the value of R Square increased by .11 at the significance level (Sig). = 0.000. This study recommended training workers to stay up to date with developments in artificial intelligence, expert systems, and neural networks in their operations, control of searching for this evidence within databases, and knowledge representation.

Keywords: supply chain agility, artificial intelligence, organizational excellence, e-commerce, Jordan

Research Title: A novel approach for modelling stress fields induced by shallow water flows on movable beds

Author: Alia Radwan Abdallah AlGhsoun, Published Year: 2025

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Faculty: Engineering and Technology

Abstract: Sediment transport in shallow waters occurs when the water flows over the bed for which the amount of generated sediments can be determined from the transport mechanism caused by the consequent flow. Recently, investigating the bedload and sediment transport using numerical models has been rapidly increased and various techniques have been developed to quantify both the hydrodynamics and morphodynamics in these systems but not the stress distributions in the deformed beds. In the present study, we propose a novel class of coupled finite element/finite volume methods to resolve the effect of sedimentary shallow water flows on the internal stresses in bed topographies. The coupled model employs the linear elasticity for the bed and the nonlinear shallow water equations for the water flow. Suspended sediments are also taken into consideration in this study, and impacts of the erosion and deposition are modelled using well-established empirical equations. The linear equations of elasticity are solved numerically using a finite element approach on unstructured meshes, while the nonlinear shallow water equations are numerically solved using a well-balanced finite volume method. We also introduce an accurate algorithm to sample forces on the interface between the water flow and bed topography to be implemented as coupling conditions between finite volume cells and finite element nodes. Distributions of stress fields in the bed topography due to erosion and sediment transport by shallow water flows are presented for several test examples. The novel coupled model is stable, efficient, accurate, well-balanced and it can be used for solving complex geometries. In addition, the proposed approach offers significant advancements in understanding sedimentary processes in shallow water environments and the induced underground stresses as a result of these processes.

Keywords: Sediment transport Erodible beds Stress analysis Linear elasticity Shallow water equations Finite element method Finite volume method

Research Title: Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview

Author: Alia Radwan Abdallah AlGhsoun, Published Year: 2024

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Faculty: Engineering and Technology

Abstract: Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics, (ii) structural health monitoring, (iii) structural design and manufacturing, (iv) stress analysis, (v) failure analysis, (vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods.

Keywords: machine learning; computational mechanics; structural health monitoring; structural design andmanufacturing; stress analysis; failure analysis;materialmodeling and design; optimization problems

Research Title: Predicting morphodynamics in dam-break flows using combined machine learning and numerical modelling

Author: Alia Radwan Abdallah AlGhsoun, Published Year: 2025

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Faculty: Engineering and Technology

Abstract: Numerical models and machine learning methods are implemented and compared to simulate and predict erosional dambreak flows and bed morphodynamics. The nonlinear shallow water equations, including sediment transport and bedload terms, are solved using a well-balanced finite volume method. Empirical erosion formulas are applied, and the obtained data train and test machine learning models. A comparative study using both computational hydraulics and various machine learning models is presented to simulate and predict erosion flows and bed deformations. The methodology is tested for a dam-break problem over an erodible bed, and results are validated against experimental measurements. The performance of various models, including Bayesian Neural Networks (BNN), K-Nearest Neighbors (KNN), M5 Trees, Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR), and Support Vector Machines (SVM), are evaluated in predicting changes in bed and free-surface profiles. In the present study, quantitative evaluations using the coefficient of determination R2 , Nash–Sutcliffe Efficiency (NSE), and normalized Root Mean Square Error (nRMSE) revealed that SVM (with R2 = 0.99 , NSE = 0.99, nRMSE = 0.0245) and BNN (with R2 = 0.98 , NSE = 0.98, nRMSE = 0.035) significantly outperformed other models, with SVM slightly better during validation and testing processes. This methodology optimizes the existing empirical models with machine learning and therefore, improving the prediction reliability for erosional dam-break flows. These findings are very important for hydraulics engineering by providing improved tools for accurate modelling and efficient simulation of sediment transport problems and thus have the potential to support practical applications in the field.

Keywords: Machine learning · Numerical modelling · Sediment transport · Erodible beds · Shallow water equations · Finite volume method