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Innovation Management Systems for Public Organizations in the UAE
No full textA Master of Science thesis in Engineering Management by Saif Juma AlFaqaei entitled, “Innovation Management Systems for Public Organizations in the UAE”, submitted in April 2023. Thesis advisor is Dr. Mahmoud Awad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Public and private organizations are striving to achieve excellence in their performance and services provided. Innovation has been at the forefront to provide new and creative ways to help public organizations achieve this. There are wide discrepancies in the planning and management of innovative programs in the UAE public sector. Little is known about the ways in which UAE organizations encourage and implement innovation management systems (IMS) to increase the quality of public services. The objective of this study is to investigate the status of IMS in UAE public organizations and provide recommendations for improvement. The main research tools used were interviews and surveys targeting innovation subject matter experts and practitioners to identify challenges and opportunities of innovation management in the UAE. Results of study suggests that more than 60% of participants believe that innovation management systems in their public organization did positively impact organization in terms of cost reduction or customer and employee satisfaction. Results also suggest that infrastructure, role of process owner, and employee participation in innovation have significant impact on innovation performance. Such results provide a significant contribution in terms of innovation management enhancement. The study recommends investing in innovation infrastructure, empowering process owners, and encouraging employees to participate to maximize the benefits of innovation.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Prediction of Drug Release Using Machine Learning Techniques
No full textA Master of Science thesis in Chemical Engineering by Ibrahim Shomope entitled, “Prediction of Drug Release Using Machine Learning Techniques”, submitted in November 2023. Thesis advisor is Dr. Nabil Abdel Jabbar and thesis co-advisor id 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
Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world
No full textThe decarbonization of industry and industrial systems is a pressing challenge given the relative lack of low-carbon options available for “hard to decarbonize” sectors such as steelmaking, cement manufacturing, and chemical production. Carbon capture utilization and storage (CCUS) represents a promising and crosscutting solution to this formidable problem. This review takes a systematic and sociotechnical perspective to examine how CCUS can support industrial decarbonization and relevant associated technical, economic, and social factors. This includes a focus on the energy and climate impacts of carbon emitting activities, the role, and options for CCUS in global responses to climate change, technical aspects of capture, transport, storage, and utilization, as well as policy implications and areas requiring further research. In doing so, the Review examines hundreds of published studies on the topic over the previous twenty years to offer a state-of-the-art investigation on technical options for capture (including direct air capture), transportation (including pipelines, ships, and rail), storage (including biotic and abiotic), and utilization (including enhanced oil recovery and biochar). The Review also investigates the evidence base within the literature on enablers and barriers to CCUS, policy mechanisms, and international frameworks as well as themes such as geopolitics, trade, and future research gaps. We conclude with insights about future CCUS pathways and sociotechnical systems dynamics.UK Industrial Decarbonisation Research and Innovation Centre (IDRIC
Automated Writing Evaluations in The ESL Classroom
No full textA Master of Arts thesis in Teaching English to Speakers of Other Languages (TESOL) by Oladiji Opeyemi Adetoyese entitled, “Automated Writing Evaluations in The ESL Classroom”, submitted in May 2023. Thesis advisor is Dr. Philip McCarthy and thesis co-advisor is Dr. Tammy Gregersen. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Automated feedback systems have taken center stage in global education. It is unarguably evident that there have been noticeable increases in attempts by many technological companies to develop automated feedback tools that support, enhance, and facilitate assessments and language learning in the classrooms. For most English as a Second Language Student (ESL) students, quality feedback is fundamental to correct use of English language for academic purpose, especially in their academic writing tasks. However, one of the major challenges has been the inability of many ESL students to access effective feedback that is timely, appropriate, and supportive. Another challenge that most ESL students face is the lack of autonomy and collaborations in the feedback review process. Most peer review and automated writing feedback tools follow a one-way communication style where the students are forced to accept the feedback passively. A solution to this ordeal of most ESL students may be found in the automated writing tool called, Auto-peer. Auto-Peer as an automated writing tool enhances effective peer review system whereby students feel comfortable to self- reflect and make correct writing decision following the timely, appropriate, and supportive guidance of Auto-Peer. This research therefore presents how Auto-Peer enhances student feedback literacy, autonomy, and student writer agency with a focus on topic sentence openers of ESL students in their academic writing.College of Arts and SciencesDepartment of EnglishMaster of Arts in Teaching English to Speakers of Other Languages (MA TESOL
Forecasting Emerging Stock Market Crashes via Machine Learning
No full textA Master of Science thesis in Engineering Systems Management by Mohammad Osama Khan entitled, “Forecasting Emerging Stock Market Crashes via Machine Learning”, submitted in November 2023. Thesis advisor is Dr. Hussam Alshraideh and thesis co-advisors are Dr. Zied Bahroun and Dr. Anis Samet. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Stock markets indicate the overall health of an economy as they play a vital role in providing a way for companies to raise capital, create new opportunities and stimulate economic growth. However, stock markets are prone to crashes and the aftermath of such an event can cause far-reaching and long-lasting effects on the economy depending on the severity which induces a need to study stock market crashes. This work explores the idea of crashes in emerging stock markets leveraging a diverse array of machine learning models, while utilizing a comprehensive dataset comprising stock market data from 32 emerging market countries, with features derived from market data, along with several engineered liquidity features. A variation of the Artificial Neural Network model is identified as the top performer displaying high accuracy, about 96.66%, with high true positive rate and low false positive rate, outperforming existing models in the literature. In industry-specific analysis, the model consistently achieved strong true positive and false positive rates, indicating acceptable outcomes for the specific industries under consideration. Furthermore, it is found, using the SHapley Additive exPlanations framework, that return along with the attributes reflecting lag, mean, and standard deviation of liquidity indicators over the past week and month significantly contribute to the prediction of crashes suggesting that stock market crashes are typically gradual processes rather than abrupt occurrences. These findings hold profound implications for risk management and investment decision-making in emerging markets, offering valuable insights for both academia and industry practitioners.College of EngineeringMultidisciplinary ProgramsMaster of Science in Engineering Systems Management (MSESM
Drivers, Challenges and Outcomes of Environmental Management System Implementation in Public Sector Organizations: A Systematic Review of Empirical Evidence
No full textOur research objectives were to conduct a systematic literature review of the empirical articles on the drivers, challenges and outcomes of environmental management system (EMS) implementation in public sector organizations (PSOs) in the Scopus database, published in English. Following the PRISMA guidelines, we identified, reviewed and analyzed 11 selected articles. We used content analysis to identify important themes and concepts. First, we mapped the selected literature. We then identified four main categories of drivers (environmentally strategic, social/stakeholders, regulatory and organizational efficiency), five categories of challenges (budgetary, human resource, technical, managerial and regulatory) and five categories of outcomes (improvement in environmental management practices, environmental performance, awareness of environmental issues, image and organizational efficiency). Finally, we identified important avenues for future research that should be explored further. This article synthesizes the knowledge on EMS implementation in PSOs and offers new insights. It will help EMS scholars and practitioners develop a broader and more critical understanding of the issues specific to EMS implementation in PSOs
Scalable Behavioral Models and Predistorters for Broad Band Power Amplifiers
No full textA Master of Science thesis in Electrical Engineering by Asma Asim Ali entitled, “Scalable Behavioral Models and Predistorters for Broad Band Power Amplifiers”, submitted in May 2023. Thesis advisor is Dr. Oualid Hammi and thesis co-advisor is Dr. Usman Tariq. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).With the ever-changing improvements in the realm of telecommunication, power amplifiers (PAs), being an indispensable component, are required to adhere to very high demands. The technology behind manufacturing power amplifiers has also improved over time but they are still prone to suffer from nonlinearities. Since amplification requires the PAs to be driven at high voltage levels, it is inevitable for them to exhibit such nonlinear behavior. This thesis was a pursuit to find a neural network (NN) based digital predistorter (DPD) to rectify the power amplifier’s nonlinear behavior. The proposed model was scalable and obliges with changing average power levels, varied bandwidth as well as heterogeneous carrier configurations of signals. The proposed neural network was assessed for behavioral modeling and showed that it is capable of accurately mimicking the memory as well as static nonlinearities of the device under test (DUT) with an average normalized mean square error (NMSE) of -29.67dB. The proposed DPD NN model was investigated for robustness with respect to the signal’s characteristics, such that the offline model does not require signal dependent updates. The signal with the highest memory effect intensity (MEI) was then proposed for the model's initial training and was found to be linearizing all the rest of the various configurations and reaching ACLR values up to -55dBc. The proposed DPD has been tested on 20MHz long-term evolution (LTE) as well as 40MHz, 30MHz, 20MHz and 10MHz new radio (NR) signals with various carrier configurations and power levels and has been observed to be meeting the 5G NR ACLR requirements. Furthermore, the proposed DPD was also trained on reduced sampling rate data to accommodate for limited hardware capabilities. It proved to be still scalable and provided satisfying linearization performance with an average ACLR of -49.29dBc.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Routing of Hybrid Truck-Drone Delivery Systems: Mathematical Models and Solution Approaches
No full textA Doctor of Philosophy Dissertation in Engineering Systems Management by Batool Mezar Madani entitled, “Routing of Hybrid Truck-Drone Delivery Systems: Mathematical Models and Solution Approaches”, submitted in March 2023. Dissertation advisor is Dr. Malick Mody Ndiaye. Soft copy is available (Dissertation, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Industrial EngineeringPhD in Engineering - Engineering Systems Management (PhD-ESM
Two-Stage Deep Learning Solution for Continuous Arabic Sign Language Recognition Using Word Count Prediction and Motion Images
No full textRecognition of continuous sign language is challenging as the number of words is a sentence and their boundaries are unknown during the recognition stage. This work proposes a two-stage solution in which the number of words in a sign language sentence is predicted in the first stage. The sentence is then temporally segmented accordingly and each segment is represented in a single image using a novel solution that entails summation of frame differences using motion estimation and compensation. This results in a single image representation per sign language word referred to as a motion image. CNN transfer learning is used to convert each of these motion images into a feature vector which is used for either model generation or sign language recognition. As such, two deep learning models are generated; one for predicting the number of words per sentence and the other for recognizing the meaning of the sign language sentences. The proposed solution of predicting the number of words per sentence and thereafter segmenting the sentence into equal segments worked well. This is because each motion image can contain traces of previous or successive words. This byproduct of the proposed solution is advantageous as it puts words into context, thus justifying the excellent sign language recognition rates reported. It is shown that bidirectional LSTM layers result in the most accurate models for both stages. In the experimental results section we use an existing dataset that contains 40 sentences generated from 80 sign language words. The experiments revealed that the proposed solution resulted in a word and sentence recognition rates of 97.3% and 92.6% respectively. The percentage increase over the best results reported in the literature for the same dataset are 1.8% and 9.1% for both word and sentences recognitions respectively.American University of Sharja
A cross-country analysis of sustainability, transport and energy poverty
No full textPoverty impacts people’s choices and opportunities and can perpetuate a disadvantaged status. Poverty remains a prevalent global issue due to disproportionate wealth distribution, which often translates to inequality in energy consumption and emissions. This research investigates if low-income households and minorities from four countries with very different national cultures, contexts, and levels of wealth experience a ‘double energy vulnerability’, a concept that simultaneously positions people at heightened risk of transport and energy poverty. Our research identifies that low-income households and minorities are at higher risk of simultaneously experiencing energy and transport poverty regardless of the national context in which they live. Our study also contests the achievement of Sustainable Development Goals (SDGs) by 2030, showing that even in relatively wealthy countries, many individuals still face energy and transport poverty. We conclude that global sustainable development requires significant shifts in policy action, resource distribution and investment in social services.Centre for Research into Energy Demand SolutionsKhalifa University of Science and Technology “High Impact Grant