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Unlocking the Power of Touch: Exploring Sensory Marketing Strategies
No full textA Master of Business Administration (MBA) thesis by Rooth Shaji entitled, “Unlocking the Power of Touch: Exploring Sensory Marketing Strategies”, submitted in November 2024. Thesis advisor is Dr. Aaron Gazley. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).In recent years, sensory marketing has witnessed dynamic growth, as companies increasingly recognize the power of appealing to consumers through multiple senses. Leveraging sensory cues has become integral to engaging customers and shaping their emotions, particularly in large-scale retail environments like malls. However, while visual and auditory stimuli have been extensively explored, the role of touch remains underexplored yet crucial, especially in online product evaluation where tactile experiences are absent. This paper seeks to address this gap by investigating the impact of tactile sensory involvement on brand perception. Through surveys, the study aims to explain how the quantity, perceived intensity, and frequency of tactile engagement influence consumers' perceptions of brands. By unlocking the potential of touch in sensory marketing strategies, this research attempts to provide valuable insights for businesses seeking to enhance consumer engagement and brand loyalty.School of Business AdministrationDepartment of Management, Strategy and EntrepreneurshipMaster of Business Administration (MBA
Machine Learning Approach for Predicting Appointment No-Show in Healthcare
No full textA Master of Science thesis in Engineering Systems Management by Dana Khalouf entitled, “A Machine Learning Approach for Predicting Appointment No-Show in Healthcare”, submitted in July 2024. Thesis advisor is Dr. Abdulrahim Shamayleh and thesis co-advisors are Dr. Hussam Alshariedeh and Dr. Mahmoud Awad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The efficiency of public healthcare delivery is essential to achieving optimal health outcomes for patients. One factor that hinders efficiency is patient no-shows, which should be managed effectively to reduce its adverse impacts on patients and healthcare providers socially and economically. In particular, it reduces patient care access, underutilizes resources, and leads to lost revenue. A no-show, or missed appointment, happens when the outpatient does not attend the scheduled appointment or cancels it at short notice. It is a common challenge faced by several healthcare systems. Previous studies have considered different models to identify patients more likely to miss their appointments; however, no study analyzed appointment no-shows in the United Arab Emirates. Therefore, this study used a data analytics and machine learning approach to develop a classification model to predict whether an outpatient will miss their appointment in Dubai's primary healthcare clinics. While data analysis is applied to extract insights from historical data and identify the most useful features, machine learning tools extrapolate on historical data to generate future predictions. A historcial dataset of appointments for the period 2021-2022 is utilized in this study. A prediction accuracy of 78% and an AUC of 0.859 were achieved using Gradient Boosted Trees while optimizing on Youden’s Index. In addition, the most influential drivers of patient no-shows were identified from the feature importances produced from the tuned model and an extensive exploratory data analysis, which included the patient’s health plan, the clinic, and the patient’s weight. As a result, recommendations of startgeies were proposed to DHA clinics to reduce no-shows, which will improve efficiency and enhance patient access to care.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
No full textOver the past few decades, notable advancements have been made in the field of transdermal drug delivery systems (TDDSs), presenting a promising alternative to conventional oral drug administration. This comprehensive review aims to enhance understanding of this method by examining various transdermal techniques, the skin’s role as a barrier to TDDS, factors affecting skin diffusion, and current challenges in TDDSs. The primary focus of this analysis centers on TDDSs utilizing hydrogels. A thorough exploration of hydrogel fundamentals, encompassing structure, properties, and synthesis, is provided to underscore the importance of hydrogels as carriers in transdermal drug delivery. The concluding section delves into strategies for hydrogel-based drug delivery, addressing challenges and exploring future directions.American University of SharjahSheikh Hamdan Award for Medical SciencesFriends of Cancer Patients (FoCP
Production of Targeted Estrone Liposomes Using a Herringbone Micromixer
No full textLiposomes are spherical vesicles formed from bilayer lipid membranes that are extensively used in targeted drug delivery as nanocarriers to deliver therapeutic reagents to specific tissues and organs in the body. Recently, we have reported using estrone as an endogenous ligand on doxorubicin-encapsulating liposomes to target estrogen receptor (ER)-positive breast cancer cells. Estrone liposomes were synthesized using the thin-film hydration method, which is a long, arduous, and multistep process. Here, we report using a herringbone micromixer to synthesize estrone liposomes in a simple and rapid manner. A solvent stream containing the lipids was mixed with a stream of phosphate buffer saline (PBS) inside a microchannel integrated with herringbone-shaped ridges that enhanced the mixing of the two streams. The small scale involved enabled rapid solvent exchange and initiated the self-assembly of the lipids to form the required liposomes. The effect of different parameters on liposome size, such as the ratio between the flow rate of the solvent and the buffer solutions (FRR), total flow rate, lipid concentrations, and solvent type, were investigated. Using this commercially available chip, we obtained liposomes with a radius of 66.1 ± 11.2 nm (mean ± standard deviation) and a polydispersity of 22% in less than 15 minutes compared to a total of ∼ 11 hours using conventional techniques. Calcein was encapsulated inside the prepared liposomes as a model drug and was released by applying ultrasound at different powers. The size of the prepared liposomes was stable over a period of one month. Overall, using microfluidics to synthesize estrone liposomes simplified the procedure considerably and improved the reproducibility of the resulting liposomes.American University of SharjahAl-Jalila FoundationSheikh Saud bin Saqr Al Qasimi Foundation for Policy ResearchPatient’s Friends Committee-SharjahBiosciences and Bioengineering Research InstituteGCC Co-Fund ProgramTakamul programTechnology Innovation Pioneer (T IP) Healthcare AwardsSheikh Hamdan Award for Medical SciencesFriends of Cancer Patients (FoCP)Dana Gas Endowed Chair for Chemical Engineerin
Content-Aware Adaptive Video Streaming Using Actor-Critic Deep Reinforcement Learning
No full textA Master of Science thesis in Electrical Engineering by Hala Nagi Amer entitled, “Content-Aware Adaptive Video Streaming Using Actor-Critic Deep Reinforcement Learning”, submitted in November 2024. Thesis advisor is Dr. Mahmoud Ibrahim and thesis co-advisor is Dr. Mohamed Hassan. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Adaptive streaming over HTTP aims to maximize user Quality-of-Experience (QoE) through video quality adaptation. Conventional adaptation schemes measure the video quality for variable bitrate (VBR) videos in terms of the average bitrate. However, video bitrate alone is not an accurate measure of perceptual quality. Alternative quality measures, such as the Video Multi-method Assessment Fusion (VMAF), can be used to better represent the quality perceived by the human eye. Studying the VMAF of video chunks across the same quality level shows that the perceived quality depends not only on the overall video bitrate, but also on the content complexity. More complex video content has a more noticeable impact on the viewer’s QoE than static content because it affects the quality perceived by the human eye more significantly. As a result, dealing with all types of content in the same way can lead to bandwidth wastage and reduced perceptual quality. This thesis proposes four deep reinforcement learning adaptation algorithms, which are the Complexity-Aware Bitrate Selection (CABS), Complexity-Aware Resource-aware Bitrate Selection (CARBS), Complexity-Aware Resource-aware Bitrate Selection with SR (CARBS-SR), and Complexity-Aware Resource-aware Bitrate Selection with Binary SR (CARBS-BSR) algorithms. Each algorithm accounts for content complexity by prioritizing complex video chunks during bitrate selection. The CABS algorithm prioritizes only VMAF, while CARBS prioritizes only data saving. The next two algorithms, CARBS-SR and CARBS-BSR, both attempt to strike a balance between the two by using super-resolution to compensate for the VMAF loss. Simulation results show the effectiveness of the proposed complexity-aware algorithms. First, CABS achieves up to a 6.4% VMAF improvement compared to the baseline algorithms at the cost of up to a 2.7% increase in bandwidth. On the other hand, the CARBS algorithm achieves up to 20% in bandwidth savings at the cost of 31% VMAF loss. CARBS-SR and CARBS-BSR achieve up to 9.6% and 18% bandwidth savings, respectively, at the cost of approximately 15% and 28% drop in VMAF, respectively.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Electronic and Magnetic Properties on Selected Transition Metal Alloys and Crystals
No full textA Doctor of Philosophy Dissertation in Materials Science and Engineering by Faisal Mustafa entitled, “Electronic and Magnetic Properties on Selected Transition Metal Alloys and Crystals”, submitted in November 2024. Dissertation advisor is Dr. Sami El Khatib and dissertation co-advisor is Dr. Mehmet Egilmez. Soft copy is available (Dissertation, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of Arts and SciencesMultidisciplinary ProgramsPhD in Materials Science and Engineering (PhD-MSE
INScription: Department of International Studies (INS) Issue #22 (April 30, 2024, Issue 8)
No full textCollege of Arts and SciencesDepartment of International Studie
Analyzing and Mitigating Cyber Threats on Elecric Vehicles Chargers for Resilient Smart Grids
No full textA Master of Science thesis in Electrical Engineering by Ahmed Abdelfatah entitled, “Analyzing and Mitigating Cyber Threats on Elecric Vehicles Chargers for Resilient Smart Grids”, submitted in April 2024. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Abdelfatah Mohamed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE