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Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity
No full textA Master of Science thesis in Engineering Systems Management by Humaid Abdalla Al Naqbi entitled, “Integration of Generative Artificial Intelligence (GAI) in Academic and Engineering Sectors to Enhance Employee Productivity”, submitted in August 2024. Thesis advisor is Dr. Zied Bahroun and thesis co-advisor is Dr. Vian Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Over the last several decades, the globe has seen remarkable growth in science and technology, which has resulted in fundamental advancements in a variety of areas and disciplines. This growth highlights the importance of artificial intelligence in human history, as it opens new horizons for leveraging advanced programs and technologies to enhance and increase organizational performance in a sustainable manner. However, despite this significant progress, there is still a research gap in the applications of Generative AI (GAI) in engineering and academic disciplines, as the challenges and opportunities associated with these fields have not been adequately studied. This study aims to fill this gap by investigating how GAI applications can be integrated to enhance productivity among students and faculty in the academic and engineering disciplines, which are vital sectors for the development of technological innovations. The research also addresses how to adopt this technology in a responsible and ethical manner, especially in these two important sectors. The study also included interviews and semi-structured surveys with faculty and students at a prestigious institution to explore their experiences, attitudes, and expectations regarding the use of Generative AI. Analyzing the data using the Relative Importance Index (RII) method, the results showed that compliance standards to mitigate bias were a top concern among faculty members, a point that was also confirmed by students. This study provides an important basis for future research aimed at guiding educational institutions towards effective and sustainable implementation of this technology.College of EngineeringMultidisciplinary ProgramsMaster of Science in Engineering Systems Management (MSESM
Adoption of blockchain technology in healthcare systems: benefits, potential applications, and challenges
No full textA Master of Science thesis in Biomedical Engineering by Tasnim Marwan Zalak entitled, “Adoption of blockchain technology in healthcare systems: benefits, potential applications, and challenges”, submitted in May 2024. Thesis advisor is Dr. Abdulrahim Shamayleh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The advancement of technology, particularly in Information technology, has brought significant changes across various sectors, with healthcare being no exception. The emergence of blockchain, a revolutionary technology, has been extensively studied in recent years as a new approach to balancing and maintaining management systems, holding considerable relevance, particularly in healthcare. This work aims at exploring the use of the complex blockchain-based system within healthcare, analyzing its impact through examining the associated benefits, challenges, and applications. Both qualitative and quantitative methodologies, including Bayesian Belief Network (BBN) analysis, have been used to identify factors that influence the adoption of blockchain in the healthcare sector. The findings reveal promising prospects for blockchain technology in healthcare, particularly in terms of its benefits and applications. However, challenges must be carefully considered and addressed during implementation to ensure successful integration. Overall, this study contributes to the growing body of research on blockchain technology in healthcare, offering insights into its potential to transform healthcare systems. The findings underscore the importance of strategic planning and mitigation strategies to maximize the benefits of blockchain while overcoming implementation challenges. Future research could further explore specific applications and evaluate the long-term impact of blockchain on healthcare delivery and patient outcomes.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
An innovative sanitary fixture for performing ablution in public facilities
No full textPurpose:
This study aims to introduce the design and the design process for an innovative sanitary fixture to be used in public facilities for the purpose of ablution. This purpose-made fixture is needed to support the hygienic, safe and comfortable performance of this essential function in public facilities in many parts of the world. The study also clarifies the need for this function and critically reviews current designs to address it.
Design/methodology/approach:
The study started by critically reviewing the standard built-in models for ablution. It also identified and analyzed new approaches to designing standalone ablution fixtures. The study then specified the characteristics of a better ablution fixture and involved drafting a design based on these characteristics, making a wooden prototype to test the design and receiving users’ feedback. The design was adjusted and tested again for more feedback. Finally, the study resulted in the development of a final design. It used digital fabrication to create the design prototype with improved aesthetics, tested it again and received user feedback.
Findings:
A survey of users showed that they found the innovative fixture more comfortable and safer than the commonly used built-in models. The main concern was the potential for water to splash on clothes from the high faucet.
Originality/value:
In addition to showing an innovative design for a purpose-made sanitary fixture for ablution, the study makes the reader aware of the various challenges of providing a hygienic, safe and comfortable facility for users to perform this function. This is very useful for the many designers and facility managers who deal with the issue.American University of Sharja
Mixed Shell Elements for Incompressible Viscoelastic Dielectric Elastomers
No full textThe focus of this work lies on modeling and simulation of thin dielectric viscoelastic structures undergoing large deformations of 100% and beyond. A shell formulation is developed that incorporates the necessary kinematic and constitutive features to capture the structure’s characteristics under transient electric loads. This includes the incorporation of not only elastic and viscous membrane strains and curvature, but also thickness deformation and variation of the electric field through the thickness. A model based on the second principle of thermodynamics is presented, which is then discretized using low-regularity shell elements in a variational setting. Computational results prove the accuracy and efficiency of the proposed method
Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine
No full textThe type of algorithm employed to predict drug release from liposomes plays an important role in affecting the accuracy. In recent years, Machine Learning (ML) has shown potential for modeling complex drug delivery systems and predicting drug release dynamics with a greater degree of precision. In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. Yet, direct comparisons of their predictive accuracy in modeling ultrasound-triggered drug release from liposomes remain limited. Existing studies predominantly focus on drug release under static conditions or with limited external stimuli rather than the dynamic, nonlinear responses observed under ultrasound exposure.Objective: This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm²). Methods: Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. Results: RF consistently outperformed SVM, achieving R² scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data. Conclusion: RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs.American University of Sharja
Advances in Liposomal Nanotechnology: From Concept to Clinics
No full textLiposomes, spherical phospholipid vesicles with a unique morphology mimicking that of body cells, have emerged as versatile nanoparticles for drug delivery. Their biocompatibility, low cytotoxicity, targeted delivery, and hydrophobic and hydrophilic characteristics make them stand out over traditional drug delivery systems. Liposomes can be tailored in size, composition, lamellarity, and surface charge, offering a unique level of customization for various applications. Extensive research in liposome technology has led to the development of a wide range of liposomal formulations with enhanced functionalities, such as PEGylated liposomes, ligand-targeted liposomes, and stimuli-responsive liposomes. Beyond their crucial role in cancer treatment, liposomes play a significant role in influenza, COVID-19, cancer, and hepatitis A vaccines. They are also utilized in pain management, fungal treatment, brain targeting, and topical and ocular drug delivery. This review offers insight into the types of liposomes, their composition, preparation methods, characterization methods, and clinical applications. Additionally, it discusses challenges and highlights potential future directions in liposome-based drug delivery.American University of SharjahDana Gas Endowed Chair for Chemical EngineeringSheikh Hamdan Award for Medical SciencesFriends of Cancer Patients (FoCP
Centralized Ranking-Based Approach to the Assignment of Electric Vehicles to Charging Stations
No full textA Master of Science thesis in Electrical Engineering by Fawzi Abdul Fattah Mohammed Moh’d entitled, “Centralized Ranking-Based Approach to the Assignment of Electric Vehicles to Charging Stations”, submitted in October 2024. Thesis advisor is Dr. Mohamed Hassan and thesis co-advisor is Dr. Ahmed Osman-Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The rapid growth of electric vehicles (EVs) has spurred the need for efficient EV-to-charging station (CS) assignment approaches. In this thesis, we provide a balanced user-utility EV assignment approach based on a ranking method, while addressing two alternative ranking methods, user-oriented and utility-oriented versions, where each serves a designated application. The main performance metric of evaluation is the average service time, defined as the average time a user spends from initiating the request until terminating the recharging service. Our approach contrasts with most EV-related studies that tend to prioritize one aspect over another, such as sacrificing user convenience for utility benefits or vice versa. Instead, we aim to balance both utility and user convenience adhering to predefined key performance indicator standards while offering alternatives that improve each aspect individually. The methodology we use depends on defining a ranking parameter between the requesting EV and all reachable charging stations and an assignment approach consisting of a central aggregator with a request accumulation period to facilitate the management of a dynamic population of EVs. The proposed ranking assignment method is compared to that of other dynamic assignment methods which are the nearest-station method, join-the-shortest queue method, and the benchmark Lyapunov EV assignment method. Our study proceeds to investigate the influence of heterogeneous EV populations on the average system time, aiming to uncover insights into the heterogeneous effects. The challenge lies in effectively managing the distribution of each EV brand in the population and addressing the varied request arrival rates stemming from diverse battery capacities. Understanding these dynamics is essential for evaluating our approach's performance under real-world conditions.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Modeling and Control of a Robot Based Rehabilitation System for the Head-Neck Joint
No full textA Master of Science thesis in Mechatronics Engineering by Ismail Tareq Raslan entitled, “Modeling and Control of a Robot Based Rehabilitation System for the Head-Neck Joint”, submitted in June 2024. Thesis advisor is Dr. Lotfi Romdhane and thesis co-advisor is Dr. Mohammad Jaradat. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringMultidisciplinary ProgramsMaster of Science in Mechatronics Engineering (MSMTR
INScription: Department of International Studies (INS) Issue #24 (September 26, 2024, Issue 2)
No full textCollege of Arts and SciencesDepartment of International Studie
Machine Learning-based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction
No full textRespiration-correlated cone-beam computed tomography (4D-CBCT) is an X-ray-based imaging modality that uses reconstruction algorithms to produce time-varying volumetric images of moving anatomy over a cycle of respiratory motion. The quality of the produced images is affected by the number of CBCT projections available for reconstruction. Interpolation techniques have been used to generate intermediary projections to be used, along with the original projections, for reconstruction. Transfer learning is a powerful approach that harnesses the ability to reuse pre-trained models in solving new problems. Methods: Several state-of-the-art pre-trained deep learning models, used for video frame interpolation, are utilized in this work to generate intermediary projections. Moreover, a novel regression predictive modeling approach is also proposed to achieve the same objective. Digital phantom and clinical datasets are used to evaluate the performance of the models. Results: The results show that the Real-Time Intermediate Flow Estimation (RIFE) algorithm outperforms the others in terms of the Structural Similarity Index Method (SSIM): 0.986 ± 0.010, Peak Signal to Noise Ratio (PSNR): 44.13 ± 2.76, and Mean Square Error (MSE): 18.86 ± 206.90 across all datasets. Moreover, the interpolated projections were used along with the original ones to reconstruct a 4D-CBCT image that was compared to that reconstructed from the original projections only. Conclusions: The reconstructed image using the proposed approach was found to minimize the streaking artifacts, thereby enhancing the image quality. This work demonstrates the advantage of using general-purpose transfer learning algorithms in 4D-CBCT image enhancement.American University of Sharja