MS Theses - DO NOT EDIT
http://hdl.handle.net/10106/25194
2024-03-29T14:09:22ZEnhancing Experiential Learning through Virtual Reality: A Case Study in System Design and Hazard Analysis
http://hdl.handle.net/10106/31769
Enhancing Experiential Learning through Virtual Reality: A Case Study in System Design and Hazard Analysis
**Please note that the full text is embargoed until 8/1/2024** ABSTRACT: The recent advancement of additive manufacturing (AM) technologies leads to an extensive need for an industrial workforce. Training in AM requires expensive capital investment to install and maintain this technology and proper knowledge about potential safety hazards. Experiential, immersive training platforms like Virtual Reality (VR) can overcome this challenge by providing opportunities for effective learning in a safe and controlled environment. VR can teach students through active participation and immersive, hands-on experiences, which is especially important for manufacturing processes involving high-risk conditions. VR can expose students to manufacturing hazards and allow them to learn through trial and error without causing any damage to real-world resources. Acknowledging the benefits of VR, two studies were developed that explore the development and evaluation of a virtual training platform for AM, explicitly focusing on selective laser sintering (SLS) printing. The platform leverages VR technology to provide undergraduate and graduate engineering students with a safe and immersive learning environment. The study begins with an in-depth literature review, examining the benefits of experiential learning and the potential of VR for enhancing engineering education. It also investigates the challenges and safety considerations associated with AM processes. Building upon this foundation, comprehensive research studies were carried out involving student participants from the Decision Analysis in the Systems Design course and the Safety Engineering course at the University of Texas at Arlington to evaluate the effectiveness of the virtual training platform. The virtual environment of the studies contains a selective laser sintering printer, a workstation with necessary supplies and safety equipment, a control panel, and information panels for process planning and hazard identification. The training platforms provide students with significant learning opportunities to gain hands-on experience with a virtual 3D printer and critical engineering skills based on operating process parameters and safety measures. The study utilizes eye metrics analysis, subjective surveys, and performance metrics to assess students' attention, engagement, learning outcomes, and satisfaction. Time-series data on eye movement and controller-based interactions, demographic information, and experience survey responses are collected. Gaze behavior analysis and subjective responses provided helpful insights into the challenges encountered by students, guiding future researchers in improving the platform's instructional design and providing assistive instructions. The outcomes of this research have practical implications for academia and industry, facilitating the training of a competent workforce capable of leveraging AM technologies. By providing a detailed exploration of the virtual training platform's development and evaluation, this study contributes to the advancement of experiential education through virtual reality.
2023-08-07T00:00:00ZCyber Risk Exposure through Supply Chain Information Network: An Application of Social Network Analysis
http://hdl.handle.net/10106/31764
Cyber Risk Exposure through Supply Chain Information Network: An Application of Social Network Analysis
**Please note that the full text is embargoed until 08/01/2024** In this paper, I study the impact of supply chain information networks on cyber risk exposure. I document that firms that are more central in the supply chain information network have higher cyber risk exposure. The rapid advancement of information and communication technology (ICT) has led to increased interconnectedness within global supply chain networks. While this enhances efficiency and profitability, it also exposes these entities to systematic and contagious risks, as cyber criminals exploit the connectedness to infiltrate multiple firms simultaneously. High-profile cyber-attacks like NotPetya, SolarWinds, and Colonial Pipeline have devastating effects on organizations and pose threats to national security. In response to these attacks, the United States government declared vulnerabilities in the supply chain network as a national emergency in 2022, leading to efforts to reinforce cybersecurity systems. However, limited research exists on supply chain factors that determine firms' exposure to cyber-attacks and cyber risk management policies. This paper contributes to the economics of cybercrime literature by exploring the interconnections of digital infrastructure among firms in the supply chain network and demonstrating the use of network theory and empirical analysis techniques to assess firm risk profiles.
2023-07-31T00:00:00ZAPPLICATION OF INTERPRETABLE MACHINE LEARNING IN FLIGHT DELAY DETECTION
http://hdl.handle.net/10106/29791
APPLICATION OF INTERPRETABLE MACHINE LEARNING IN FLIGHT DELAY DETECTION
Precise flight delay prediction is vital for the airline industries and passengers. This thesis focuses on applying several machine learning and auto-ML techniques to predict flight delays. A flight delay is said to occur when an airline lands or takes off later than its scheduled arrival or departure time, respectively. Conventionally, if a flight's departure time or arrival time is greater than 15 minutes than its scheduled departure and arrival times respectively, then it is considered that there is a departure or arrival delay with respect to the corresponding airports. Notable reasons for commercially scheduled flights to be delayed are adverse weather conditions, air traffic congestion, a late reaching aircraft to be used for the flight from a previous flight, maintenance, and security issues. In this research study, a python-based model will be developed for a specific Airline and an Airport from already established models that are available in literature and were implemented in flight delay predictions. Once that is completed, the same model will be used for a different Airline at the same Airport. Later, the model will be implemented for several other Airports to check the adaptability of the models. In this process, there will be an attempt to enhance the existing models by carefully selecting the dataset and features. In the final stage, the results will be compared with the Microsoft Azure Machine Learning Studio, the best model will be deployed using Auto-ML and the existing interpretable machine learning package, LIME will be used to explore local prediction capability of the models. This study has been conducted with the hopes that alongside other increasing numbers of studies in this subject matter, it will contribute to improving on-time performances of flights to benefit airline customers, airline personnel, and airport authorities.
2021-05-06T00:00:00ZTECHNOLOGY INCREASES EFFICIENCY IN THE WORKPLACE, BUT OFTEN CREATES CHAOS IN THE PROCESS
http://hdl.handle.net/10106/26753
TECHNOLOGY INCREASES EFFICIENCY IN THE WORKPLACE, BUT OFTEN CREATES CHAOS IN THE PROCESS
There is a multitude of technology solutions used in the typical workplace, aimed at increasing efficiency, but often chaos is created in the process. Can this chaos be reduced or eliminated? This paper seeks to answer that question. Specific technologies used in workplace environments and how they are implemented, including deployment, project management, and end-user involvement are dissected for a common thread.
Underestimating the importance of communication is the common thread identified when chaos follows the implementation of new technology. The end-user requires constant communication before, during, and after the implementation process, whether the news is good or bad. Without this communication, the negative push-back from the end-users to the new technology can eliminate any desired increase in efficiency in the workplace.
2017-06-06T00:00:00Z