The purpose of this thesis is to suggest the application of Active Noise Cancellation in Air Force pilot headset and helmet not only to reduce noise-induced hearing damage, but also to enhance pilot performance and aviation safety. Despite the recent advances of sound treatment technology, the interior sounds of a military aircraft exceed the damage risk criterion of 8 hrs/day exposure. Air Force pilots are flying under the extreme condition where noise is severe and prevalent. The exposure to noise can lead to permanent hearing loss, stress and fatigue, unintelligible communication, and deterioration of speech perception and recall. With all this in mind, the negative effects can result in the decrement of pilot performance which is foremost for the aviation safety since flight missions performed by Air Force pilots require robust concentration, analytical inference, accurate and appropriate movement, reliable performance, and long-lasting attention. The Republic of Korean Air Force (ROKAF) has introduced Passive Noise Cancellation (PNC) into conventional pilot helmets to deal with the issues above; however, it has not been proven yet that whether PNC is effective. On top of that, compared to cargo/helicopter pilots who can use aviation headset with Active Noise Cancellation (ANC), most fighter pilots are still using conventional pilot helmet without ANC except for F-35 fighter pilots. This research aimed (1) to analyze the effect of cockpit noise on hearing health, pilot performance, and aviation safety, (2) to examine the effectiveness of PNC in conventional pilot helmet, (3) to evaluate the usefulness of ANC in military aircrafts, and (4) to suggest the use of ANC in conventional fighter pilot helmet.

The fourth industrial revolution, or Industry 4.0, is essential to the success of Aerospace and Defense (A&D) organizations as it showcases the ability to adapt, innovate, and remain competitive. Industry 4.0 technologies such as cyber-physical systems (CPS), big data, cognitive computing, smart factories, connected manufacturing, and the Internet of Things (IoT) focus on revolutionizing manufacturing through embedding digital and physical systems, with the goal to maximize the desired output(s) while using minimal resources. Although there are numerous advantages, there are challenges associated with implementation of these complex systems. This doctoral research investigates the critical factors needed for successful implementation of Industry 4.0 in A&D. A systematic and Thematic Analysis (TA) of the applicable literature revealed this area of research has significant opportunities for advancement and further examination. The review identified 12 initial factors and an implementation outcome. These factors were further assessed through conducting a survey with industry experts. The ten emergent factors, their interrelationships, and impacts on the outcome variable were examined using multiple linear regression and correlation analyses. This assessment revealed interaction amongst emergent factors is essential and resulted in three Critical Success Factors (CSFs): Documentation & Governance, Resource Allocation, and Workforce Involvement. These factors reiterated embedding documented strategic guidance for implementation, ethical standards, and updated uniform policies across all organizations is crucial. Further, ensuring resources such as funding for required items and adequate time to perform associated tasks, is also vital for success. The research also showed involvement of the workforce in implementation efforts, including participation in decision-making activities and being knowledgeable about the overall implementation plan, is another critical component. Following this framework and noting the resulting CSFs, the potential benefits and successful implementation of Industry 4.0 technologies is more accessible to the A&D industries.

This study aimed to develop a framework for the U.S. Navy's leadership styles by assessing self-perceived leadership styles in a sample of reserve Engineering Duty Officers (EDOs) and the Senior Enlisted Leaders (SELs) in these technical units. Transformational and transactional leadership styles were examined using the Multifactor Leadership Questionnaire (MLQ) Form (Avolio & Bass, 2004), while the servant leadership style was analyzed using the Servant Leadership Self-Assessment Questionnaire (SLSQ) (Sandling, 2021). The effect of rank and the triad leadership position on leadership style was explored in this study. The survey was made available to all EDOs and SELs (n = 525). A total of 84 surveys were completed (a 16% return rate). Results showed a statistically significant difference in servant leadership between SELs and junior officers. When broken down into the factors of servant leadership, this difference was seen in the conceptual skills and putting followers first factors. No statistical differences were seen across leadership triad positions or between any other ranks for any other leadership style. The findings are discussed for their implications for leadership development in the U.S. Navy.

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Virtual and Constructive (VC) simulations can be used to measure weapon effectiveness and support the capture of the operator's behavior. These areas are very non-linear, and Artificial Intelligence (AI) can support better analysis due to its way of dealing with incomplete information, noise, and nonlinearities. VC simulations focus on scenario variation and sensitivity analysis to find critical factors of success. This variation is a significant necessity for reducing bias. These scenario variations and the relative easiness to program numerous scenarios makes VC an excellent candidate to generate data from different sources. The combined data can be processed by an analytics tool, using AI/machine learning, to build useful models that can be used to study weapon effectiveness (and extrapolating beyond linear models) and build behavioral models that can encapsulate behavior. This encapsulated behavior can be used in other scenarios and study ways to enhance training or counterattacks. The dissertation ends with conclusions, limitations of the research project, contributions to the body of knowledge, and further research. The issues of new research go in details of potential methodologies using VC and AI/machine learning.

Cooperation is the hallmark human trait which has allowed us to congregate into the vast, continent-sprawling societies we live in today. Yet, the precise social, environmental, and cognitive mechanisms which enable this cooperation are not fully understood. Toward this, lucrative insights have been borne through the use of formal computational models of socio-cognitive phenomena: In simulating our own cooperative behavior, we can better deduce the exact factors which cause it. The combined knowledge of these factors and ability to computationally simulate them allows us to further two goals: First, it empowers us with the knowledge of how to modify our social systems to better human well-being and promote more sustainable, equitable, and compassionate societies. Second, the computational aspect allows us to more directly create artificial, socially competent companions—whether robotic or entirely digital—to cooperate with us in the real world in achieving the first goal. In this thesis, I contribute to the development of artificial social cognition by examining two case studies of cooperation dilemmas: a game of social team cooperation inference known as stag-hunt, and a stylized cooperative irrigation system. Specifically, I show causal, generative models encoding hypotheses on actual mechanisms in the human mind which are able to outperform the extant state-of-the-art models in both of these cases. In the second case, I show how models like this can be automatically discovered through an algorithm known as evolutionary model discovery, greatly expediting the deduction of new models in similar domains. The results have implications not only for understanding the dynamics of human teaming and irrigation systems (the humans in algorithms), but also broader human socio-cognitive mechanisms contributing to cooperation (the algorithms in humans)—all while simultaneously allowing these mechanisms to be encoded into socially competent AI.

The advances in technology in the recent decade have led to the introduction of a new term called Industry 4.0, which has led to the emergence of the term lead to Quality 4.0. Quality 4.0 is the digitalization of traditional quality approaches and focuses on digital tools used to improve an organization's ability to meet customer requirements with high quality. The application of Quality 4.0 practices in higher education institutions (HEIs) has the potential of driving innovation through technological advances in various aspects. This study aims to develop a framework to assess the maturity level of HEIs towards transformation to Quality 4.0 while considering the impact of such transformation on the process, people, and technology dimensions defined in the Quality 4.0 model proposed by the LNS Research. 95 practices were categorized into 11 Quality 4.0 axes to form a survey that determines the current level of HEIs transformation efforts and finds their strengths and weaknesses. As a result, the investigated HEIs will be located at one of the five maturity levels defined by the Quality 4.0 Maturity Scale (Q4.4-MS). The proposed framework has been validated by determining the maturity level of Quality 4.0 implementation within Saudi HEIs. The Friedman test was performed to statistically ensure the framework's ability to differentiate between the maturity levels of adopting Quality 4.0 observed in the study sample. The result showed that all 26 Saudi HEIs obtained scores ranging from the second to the fifth levels of the Q4.0-MS. This finding indicates that some HEIs have a basic understanding of digital while others are at the most advanced level of Quality 4.0 adoption. The performed analysis confirmed the applicability of the developed framework for assessing the level of adoption of Quality 4.0 axes implementation at any population of HEIs.