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

Research Title: Factors Impacting Women Gaining Leadership Roles in the Jordanian Construction Sector: Architects and Civil Engineers

Author: Alaa Saleh Al Shdiefat, Published Year: 2024

Buildings, 14

Faculty: Engineering and Technology

Abstract: Abstract: The persistent underrepresentation of women in leadership positions within the construc tion industry remains a global concern. In Jordan, despite comprising 60.45% and 22.4% of the total workforce of architects and civil engineers, respectively, women’s contribution to management roles is significantly low. Therefore, there is an urgent necessity to examine the factors hindering women’s advancement in the construction sector and their ability to attain leadership positions. This research aims to provide an overview of the current situation in Jordan, focusing specifically on the architectural and civil engineering professions. It presents findings from a desktop study, a survey questionnaire, and focus groups. The Severity Index (SI) formula is utilised to identify critical barriers in the Jordanian context, derived from both the literature review and questionnaire responses. Addi tionally, the Interpretive Structural Modelling (ISM) technique is employed to establish a hierarchy of critical barriers and analyse their interrelationships. The study reveals that the obstacles impeding women from assuming leadership roles in the Jordanian construction sector primarily stem from 20 critical barriers categorised across 11 levels in ISM. Notably, the lack of childcare programmes is identified as a fundamental barrier at the lower level, while informal networks formed by men emerge as the highest-rated barrier at level 11. Addressing and mitigating these challenges is crucial to facilitating women’s progression into leadership positions within the sector and is anticipated to contribute significantly to addressing the growing complexity of modern construction projects.

Keywords: construction; women; leadership; Jordan; barriers

Research Title: ISM Model for Assessing Critical Productivity Factors in the Jordanian Construction Industry Post-COVID-19 Pandemic

Author: Alaa Saleh Al Shdiefat, Published Year: 2024

Organization, Technology and Management in Construction: an International Journal, 16

Faculty: Engineering and Technology

Abstract: Abstract: The construction industry is a human-intensive industry despite the massive development in technologies. Nowadays, after crossing Covid 19 pandemic, the construction industry is an important sector for saving the national economy. The Covid 19 pandemic creates new ways of thinking due to massive and unpredictable socioeconomic consequences. Thus, understanding the critical productivity factors after Covid 19 pandemic will enhance the construction industry by improving the understanding of the professionals who involve at an early stage of the project lifecycle. This study aims to determine the critical productivity factors after Covid 19 pandemic for enhancing the construction industry in developing countries such as Jordan. Review of available literature that similar to the related topics before Covid 19 pandemic were explored, and then a questionnaire was distributed across the Jordanian construction industry to determine the main productivity factors post-covid 19 pandemic. A focus group conducted to determine the interrelationship among the factors using the Interpretive Structural Modelling (ISM) approach. The obtained results indicated twenty-two main productivity factors are affecting the Jordanian construction industry. These factors hierarchy is categorised over six levels of ISM whereas the sixth level has the greatest factors influencing productivity in construction industry. Thus, enhancing productivity in construction projects asks to solve problems related to factors in level 1, which will help to solve problems at the next level and so on.

Keywords: Construction, Productivity, Factors, Jordan, Post-COVID-19 pandemic

Research Title: Pain and anxiety in patients with breast cancer treated with morphine versus tramal with virtual reality

Author: Maha Mohammed Wahbi Atout, Published Year: 2024

HealtH Care for Women InternatIonal, 45

Faculty: Nursing

Abstract: The treatment of pain and anxiety in cancer patients includes both pharmaceutical and non-pharmacological approaches. The researchers of this study aimed to compare the effectiveness of morphine versus Tramal with virtual reality therapy (VR) in reduc- ing pain and anxiety in female patients with breast cancer. The sample was composed of 80 women with breast cancer who where treated at a specialized cancer center in Jordan. A quasi-ex- perimental design was used in the study intervention. When used with VR, the tramal analgesics did not differe significactly from the effect of morphine in reducing the pain and anxiety. However, both groups had a significant drop in the level of pain and anxi- ety. When combined with VR, the use of weak opioids such as Tramal will have nearly the same effect as strong opioids such as Morphine in reducing pain and anxiety in breast cancer patients.

Keywords: Pain, anxiety, breast cancer, morphine, tramal,virtual reality

Research Title: نحو نظرية فلسفية في مواجهة التطرف والعنف والإرهاب

Author: Amani Ghazi Jarrar, Published Year: 2025

أفكار, 433

Faculty: Arts

Abstract: تتناول الدراسة نموذجا من النظريات الفلسفية المختصة بقراءة التاريخ والايديولوجيا التي تتصدى للتطرف والعنف والإرهاب. وتتخذ الدراسة النهج الفلسفي في التحليل، حيث تستدعي النظريات المفسرة للعنف والارهاب عبر التاريخ. وتتخذ الدراسة كل من النموذج العربي والغربي في التحليل المقارن. وتطرح الدراسة السؤال الفلسفي المتعلق بأثر النظرية الفلسفية في قراءة التاريخ ولا سيما نهاية التاريخ نهاية الايديولوجيا وتحلل علاقة ذلك بالحرية والليبرالية في مقابل العنف والارهاب. وتخلص الدراسة الى جملة من الثوابت التي تأصلت عبر التاريخ والتي كان لها الأثر الاكبر في مواجهة صراع الحضارات، وقد أثر ذلك على حالة كراهية الآخر والإرهاب. وأوصت الدراسة بضرورة العمل فكرياً من أجل استئصال أصول الكراهية والعنف والتطرف في جذور العقل المستقيل.

Keywords: نظرية فلسفية، قراءة التاريخ، التطرف، العنف، الإرهاب، ايديولوجيا.