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Shear Strength Behaviors of Tire Shred-Dune Sand Admixtures
No full textA Master of Science thesis in Civil Engineering by Ruba A. Elmootassem entitled, “Shear Strength Behaviors of Tire Shred-Dune Sand Admixtures”, submitted in January 2024. Thesis advisor is Dr. Magdi El-Emam. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The use of discarded tires in civil engineering applications has been shown to be an effective tire waste management technique. Scrap tires also act as a soil stabilizer capable of improving shear strength parameters, bearing capacity, and overall stability of soil. While dune sand is abundantly available naturally in the UAE, its potential for use in geotechnical engineering applications is often underutilized because of its poor engineering properties. Mechanical stabilization of sand with the inclusion of tire shreds has been shown to improve its shear strength properties for use as backfill material. While studies have evaluated the effects of tire shred content and size using large-scale direct shear (LSDS) tests, the available literature provides inconsistent results in terms of optimum scrap tire content and size for maximum shear strength improvement. The objective of this research is to assess the suitability of using tire shred-dune sand admixtures as backfill material by evaluating the shear strength behaviour of the soil matrices at different tire shred/dune sand ratios and tire shred sizes. This isolates the optimum tire shred-dune sand mixing ratio and suitable tire shred length for use as backfill. The major soil parameters investigated were admixture shear strength, friction angle, and stiffness. A large-scale direct shear (LSDS) apparatus was designed and manufactured for use at AUS geotechnical engineering lab. The apparatus was used to test 45 tire shred-dune-sand samples, with tire shred contents of 0, 5, 10, 15, and 20% by weight, and tire shred lengths of 30-50mm, 50-70mm, and 70-100mm. The results showed that the inclusion of tire shreds in dune sand can increase peak shear strength by up to 77% as compared to pure dune sand. The optimum tire shred size was concluded to be 50-70mm as it produced the highest peak and residual shear strength and friction angles. TS contents between 15 and 20% consistently produced the highest shear strength parameters, with a peak friction angle of 39.5o achieved at optimum conditions. The highest residual friction angle of 40.8o was achieved at TS content of 10%. The results also showed that increasing TS content and length decreases stiffness for most tested samples. These findings provide major insight into the feasibility of using tire shreds as a dune sand stabilizer, specifically in the UAE, and can be used to further develop sustainable engineering practices in the region.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Hand-Crafted Features with Simple Deep Learning Architectures for Human Activity Recognition
No full textA Master of Science thesis in Computer Engineering by Yaman Sufian Albadawi entitled, “Hand-Crafted Features with Simple Deep Learning Architectures for Human Activity Recognition”, submitted in June 2024. Thesis advisor is Dr. Tamer Shanableh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).With the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a hand-crafted feature extraction technique associated with a deep learning method for classification. In this work, we divide the sensor sequences time-wise into non-overlapping 2D segments. We then compute statistical features from each 2D segment using two approaches; the first approach computes features from the raw sensor readings, while the second approach applies time-series differencing to sensor readings prior to feature calculations. Applying time-series differencing to 2D segments helps identify the underlying structure and dynamics of the sensor reading across time. We also experiment with two selection methods, including stepwise regression and selecting KBest to select useful features in an attempt to create a more representative model of the extracted features. Also, we investigate the effect of adding a one-dimensional convolutional layer and an attention layer to the deep learning network on the model performance. We experiment with different numbers of 2D segments of sensor reading sequences. We also report results with and without the use of different components of the proposed system. The proposed feature extraction method is integrated with an existing transformer designed for human activity recognition. All of these arrangements are tested with different deep-learning architectures. Several experiments are performed on four benchmark datasets: mHealth, USC-HAD, UCI-HAR, and DSA. The experimental results revealed that the proposed system outperforms the human activity recognition rates and F1-scores reported in the most recent studies. Specifically, we report recognition rates of 99.17%, 81.07%, 99.44%, and 94.03% for the four datasets, respectively.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE
Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
No full textA Master of Science thesis in Biomedical Engineering by Maryam Haniyeh entitled, “Application of Data Mining to Predict and Diagnose Diabetic Retinopathy”, submitted in June 2024. Thesis advisor is Dr. Michel Pasquier and thesis co-advisor is Dr. Assim Sagahyroon. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Diabetes Mellitus (DM), a chronic metabolic disorder, is characterized by high blood sugar levels that can lead to complications such as Diabetic Retinopathy (DR)—a condition that damages the retina and can cause vision loss. The early detection and management of DR are critical and can be facilitated by a comprehensive understanding of the disease and its risk factors, achievable through advanced data mining techniques. This study sets out to construct data mining models that can identify and associate these risk factors with the likelihood of developing DR. The dataset for this research was sourced from Saqr Hospital in Ras Al Khaimah and includes 400 patient records, with 194 patients diagnosed with DR. In assessing the impact of various factors on DR, the study will analyze 29 different attributes including diabetes duration, Body Mass Index, blood glucose levels, cardiovascular disease, hypertension, and others. The initial analysis employed supervised classification algorithms such as k-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Random Forest, XG-Boost, and J48 Decision Tree to predict the incidence of DR. To enhance the model’s accuracy, 10-fold cross-validation was used, allowing the model to learn from different subsets of the data. Feature selection was utilized to determine the specific attributes that correlate with the presence of DR. Moreover, unsupervised learning techniques were employed to discover association rules and evaluate the probability of relationships within the dataset. The results indicate that feature selection significantly improved the performance of the classifiers, with the Random Forest algorithm achieving the highest accuracy of 91% and specificity of 90.4%. Moreover, the unsupervised learning methods highlighted strong associations between hypertension, diabetic macular edema, and DR. These findings can help in understanding the interconnected nature of these complications and emphasize the importance of comprehensive management approaches for patients with diabetic retinopathy.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
INScription: Department of International Studies (INS) Issue #21 (March 28, 2024, Issue 7)
No full textCollege of Arts and SciencesDepartment of International Studie
Numerical Analysis of Mechanical Behavior Using Bio-Compatible Material for Dental Prosthesis
No full textRecent technological advancement has revolutionized the field biomedical engineering and has significantly enhanced the well being of human life. From the design and manufacturing of bio-compatible materials to the development of software for computer-aided design (CAD) modeling and simulation, biotechnology plays a crucial role in creating safe and effective dental implants. Finite element analysis has been accustomed to simulating the stresses and strains on implants helping to optimize the design and insertion process. This study aims to compare the stress profiles of Polyetheretherketone (PEEK) and Titanium alloy dental implants during different stages of implant insertion depths. The study builds bone-blood interface CAD models and performs numerical simulations. The results indicate that PEEK is potentially capable of replacing Titanium alloy as a suitable material for dental implants. Additionally, the study evaluates von Mises stresses in cortical and cancellous bone and considers the impact of torque and insertion depth on stress profiles, as well as strain and deformation calculations. The results provide an insigt into the usage of biocompatible materials in dental implants
Physical Asset Management for Critical Utilities— A Systematic Literature Review
No full textAsset management plays a significant role in ensuring the system availability and sustainability of utilities, encompassing critical sectors such as electricity, water, and wastewater. An efficient asset management is a linchpin that safeguards a resilient service to the community through system availability maximization and service disruption avoidance. This study attempts to provide a systematic review of recent literature focused on the asset management of utilities, including asset lifecycle stages and management models. Based on the review, several research gaps that warrant further investigation is highlighted and verified by subject matter experts. Literature review was organized based on the four lifecycle stages of any asset which are planning, acquisition, operations and maintenance, and finally, disposal. A total of 249 articles were included and reviewed as part of this study. Most of articles reviewed tackles asset management in general and few focused on utilities’ physical asset management. Despite the importance of all four stages of assets lifecycle, researchers focused more on asset maintenance with less attention paid to the other stages. The review also highlights the need to develop a holistic asset management maturity model to identify gaps and advocate performance improvement actions. Further research on recent important development such as resiliency, digital transformation, acquisition and outsourcing strategies and their impact on assets is warranted.Open Access Program through the American University of Sharja
Proactive Fault Tolerance and Minimizing Task Execution Failure in A Cloud Data Center
No full textA Master of Science thesis in Computer Engineering by Ayman Kandil entitled, “Proactive Fault Tolerance and Minimizing Task Execution Failure in A Cloud Data Center”, submitted in December 2024. Thesis advisor is Dr. Ra’afat Abu-Rukba and thesis co-advisor is Dr. Salam Dhou. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE
Teacher identity continuum: A framework for teacher identity shifts online
No full textIn March 2020, due to COVID-19, English faculty in higher education institutions (HEI) in the United Arab Emirates (UAE) had to migrate to and administer online courses despite limited familiarity and training in online delivery. Moving online, teachers had to negotiate their long-held beliefs, teaching practices and roles as they navigated a new educational context, thus further reinterpreting their professional identities. In the face of change, teachers may experience a sense of insecurity that influences their identity development, and research is still early in understanding teacher identity formation, factors impacting identity changes, and the role of identities in teachers’ motivation and learning (Schutz et al., 2018). Therefore, this research draws on identity theory to examine how 14 English faculty members in HEIs in the UAE negotiated their beliefs, roles, and practices as they shifted online due to the pandemic. Through a qualitative exploratory multimethod approach, including mind maps and semi-structured interviews, and thematic analysis, my findings led to the development of a new framework instrumental in understanding the reshaping of teacher identities through the forced transition from FTF to online teaching. My research positioned teachers’ online identities on a Teacher Identity Continuum (TIC) with Digital Adapters, Digital Resisters, and Digital Ambivalents, including a spectrum of related beliefs, roles and practices. This framework has several practical implications for teachers, teacher education, and institutional leadership as they manage transitions and times of change
Fundamentals of Digital Design - ARC 265
No full textSyllabus for the Department of Architecture course "Fundamentals of Digital Design - ARC 265", by Instructor(s) Roberto Castillo for the Summer 2023 semester.College of Architecture, Art and DesignDepartment of Architectur
Folated Liposomes and Ultrasound to Induce the Controlled Release of a Model Drug
No full textA Master of Science thesis in Chemical Engineering by Wafa Nasser Mohammed Ba-Hutair entitled, “Folated Liposomes and Ultrasound to Induce the Controlled Release of a Model Drug”, submitted in April 2023. Thesis advisor is Dr. Ghaleb Husseini. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Chemical EngineeringMaster of Science in Chemical Engineering (MSChE