There are currently about 466 million people worldwide who have a hearing disability, and that number is expected to increase to 900 million by 2050. About 15% of adult Americans have hearing disabilities and about every three in 1,000 U.S. children are born with hearing loss in one or both ears. The World Health Organization (WHO) estimates that unaddressed hearing loss poses an annual global cost of $980 billion, including cost of educational support, loss of productivity, and societal costs. These are all evident that people with hearing loss are experiencing several kinds and levels of difficulties. In this dissertation, we are addressing two main challenges of hearing impaired people; sign language recognition and post-secondary education. Both sign language recognition and reliable education systems that properly support the deaf community are essential needs of the globe and in this dissertation we aim to attack these exact problems. For the first part, we introduce novel dataset and methodology using machine learning while for the second part, a novel agent-based model framework is proposed. Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this dissertation, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image's facial expression could not be described by any of the emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems. In addition, post-secondary education persistence is the likelihood of a student remaining in post-secondary education. Although statistics show that post-secondary persistence for deaf students has increased recently, there are still many obstacles obstructing students from completing their post-secondary degree goals. Therefore, increasing the persistence rate is crucial to increase education and work goals for deaf students. In this work, we present an agent-based model using NetLogo software for the persistence phenomena of deaf students. We consider four non-cognitive factors: having clear goals, social integration, social skills, and academic experience, which influence the departure decision of deaf students. Progress and results of this work suggest that agent-based modeling approaches promise to give better understanding of what will increase persistence.

Obsolescence is an unavoidable reality in manufacturing systems and supply chain environments as systems are needed to be sustained for longer and longer periods of time. These extended life cycle products include airplanes, ships, industrial equipment, medical equipment, and military systems. The United States military has coined this issue as Diminishing Manufacturing Sources and Material Shortages (DMSMS). Research shows that the main areas of concern for obsolescence are cost optimization, obsolescence management, system life cycle, design/system refresh planning, architecture/open systems, and end-of-life (EOL) predictions. This effort suggests a need for a more effective management approach to tackling obsolescence with an emphasis on proactive management. The goal of this research was to create an obsolescence management framework for the purpose of managing obsolescence issues with military based systems. This research shows the potential for using machine learning as a life cycle forecasting tool over traditional data mining tools. The results for this small-scale case study show promising results for a larger scale experiment. Another powerful proactive strategy using machine learning is building technology refresh cycles into a system based on obsolescence risk levels. Some key areas of focus for a strong framework are funding for a robust DMSMS team, a robust supply chain, system design that factors in obsolescence risk, and consistent communication with all parties involved. It is imperative to develop an effective and data-driven approach to communicating obsolescence impacts to leadership to ensure successful mitigation of obsolescence issues. Some post-case tools and strategies include utilizing sustainment, production, and technology refresh roadmaps, along with employing data driven metrics to provide key information to leadership and demonstrate value to the customer. This study demonstrates opportunities and challenges for entities dealing with component obsolescence, methods for minimizing the issues that go along with it, and identifies best practices for obsolescence management.

Business and engineering have long intersected with each other in industry. In actuality, they are inseparable. That notion drove the thought process and actions taken to observe phenomena within a U.S. Fortune 500, Aerospace & Defense industry, Department of Defense, independent contractor. In Aerospace & Defense, the ability to implement technology freely and change to address an ever-evolving technological landscape in the world has proven to be difficult given the nature of the work performed. U.S. national security must be protected at all times, therefore information sharing guided by a "need-to-know" basis create an inability to easily implement organizational change company wide. The study focused on how to perform organizational change. Specifically, implementation of industry 4.0 techniques and technology within a classified organization given a shortened planning horizon and window of time to create change of two business quarters defined as 180-days. Through specific selection criteria three programs were chosen for observation and implementation of discovered necessary changes. Each program had their own respective size, nature, and type. Due to national security reasons, they will be defined for the purposes of this dissertation as Program A, the large program, Program B, the medium program, and program C, the small program. By developing, and then executing, a 10-step theoretical framework named the Ervin Change Sustainment Model, organizational change was sought after to introduce industry 4.0 techniques and technology to the major product line observed within the independent contractor.

For many years, natural resources have been used as the main input of production process in countries around the world, which has caused many problems to our planet such as rapid climate change, loss of biodiversity, drastic environmental events, and social problems. In recent years, pressure on countries to transition to cleaner production processes to mitigate problems arising from using natural resources has increased. As a result, green products have become a point of interest due to their low environmental impact, and green product development has become an important part of green growth policies for many countries. Green product development requires technology, capital, infrastructure, and skills which are not evenly distributed among countries, therefore, the capability to develop green products are not the same between countries. The purpose of this dissertation is to explore the evolution of green product development in 61 countries between 2003 and 2015 and explore the effect of this development on their overall green growth performance. To this end, this dissertation is designed based on the concept of product space and its main hypothesis of path-dependent economic growth. It employs an algorithm based on network science theory to identify patterns of green product development, and uses Partial Least Squares Structural Equation Modeling (PLS-SEM) as a statistical method to test the effect of green product development on Countries' Overall Green Growth Performance (COGGP) and suggest activities that can be targeted to foster countries' green product development and overall green growth performance. The results of this dissertation show countries followed the path-dependent economic growth to develop new green products, and at the same time for considerable amount of new green products, countries followed a process, non-path dependent green economic growth, to develop new green products and expand their green production baskets. In addition, the results show empirically that investment in innovating environmental related technologies enhance countries' overall green growth performance and green product development based on path-dependent economic growth hypothesis, but it is not enough to entirely eliminate the need for technology, capital, infrastructure, and skills for green product development.

Continuous improvement (CI) is an initiative to improve the performance of processes in alignment with the customer needs and organizational strategy. Lean Six Sigma (LSS) is one of the most successful CI techniques in redesigning and improving significant processes to improve quality and eliminate waste. The healthcare sector has benefited from applying LSS due to its complicated work practices that face many challenges including increased expenditures and difficulties related to individual or community access to appropriate care. In particular, Emergency Departments (ED) have an important unit within healthcare organizations due to their essential role in providing urgent medical care services to patients. The aim of this doctoral research study is to develop a theoretical model using grounded theory to investigate the factors for successful LSS implementation in ED including how ED work environment affects the reduction of patient length of stay, which is one of the biggest issues that ED face. Therefore, the main objectives of this research are to: (1) investigate trends in the research area using systematic literature review, (2) develop an Initial Conceptual Framework including identifying the relationships between the variables of LSS implementation, (3) use an expert study where a group of experts will provide additional evidence regarding LSS implementation, and (4) test the model using survey questionnaire that examines the behavior of the variables. This research will be documented as a manuscript-style dissertation including four peer-reviewed academic journal articles each summarizing the results from a phase of this research. The results of this research will provide a conceptual model to guide the implementation of LSS in ED bringing the potential benefits of this approach to a critical department in healthcare organizations. Further, this research will inform future research by investigating the work environment effects on application of LSS.

Circadian rhythms are 24-hour fluctuations determining periodicity in a wide range of physiological processes, including neural activity and hormone secretion, which controls sleeping and feeding habits. Despite significant diurnal variation in human brain function, neuroscientists have rarely considered the effects of time-of-day on their studies. Moreover, there are interpersonal discrepancies in sleep-wake patterns, diurnal preferences, and daytime alertness (known as chronotypes), which can cause different diurnal profiles in human cognition and brain performance. The study of circadian typology differences has increased in recent years, however, examining the effects of both time-of-day and people's chronotype requires further elucidation. In the present study, we performed graph-theory based network analysis on resting-state functional MRI (rs-fMRI) to explore the topological differences in whole-brain functional networks between the morning and evening sessions, as well as between extreme morning-type and evening-type participants. To that end, 62 individuals (31 extreme morning- versus 31 evening-type) underwent two fMRI sessions: about 1 hour after the wake-up time (morning) and about 10 hours after the wake-up time (evening), scheduled in accord with their declared habitual sleep-wake pattern on a regular working day. The findings of this study revealed the effect of time-of-day on the functional connectivity patterns, and there was no significant difference in chronotype categories. Compared to the morning session, we found relatively higher network segregation (i.e., higher small-worldness and modularity) and higher synchronization in the evening session. Interestingly, local graph measures were altered predominantly across the left hemisphere in areas involved in language processing, sensorimotor control, as well as subcortical portions of the limbic system.

Leadership is one of the most important factors in organizational success. Innovation is another of most important factors in organizational success. Leaders play a pivotal role in the innovation capabilities of organizations. One of the most important areas in Leadership Studies (LS) is the association between leadership style and the firm's innovation performance, but a lack of understanding and consensus still remains as a major issue. This research aims to address the research gap by reviewing the empirical literature and determining how the ambidextrous leadership (transactional and transformational) styles in top level U.S. management (CEOs) are related with firm ambidextrous innovation (exploitative and exploratory) performance in ambidextrous US firms. This research employs a survey instrument, based on established research, to employees of U.S. companies and ask them about their perception of their respective CEO's and the degree of innovation in their firm. We control for variables such as organizational size and how long they have been in operation. Ultimately, leadership has often been seen as a linear model and often based on one style of leadership, however we seek to understand when certain types of leadership can help over other types for particular types of innovation, and how certain types of innovation may call for certain types of leadership.

In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and became a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, the COVID-19 pandemic resulted in a significant loss of human life in 2020 and 2021. To better understand the pandemic, this doctoral research incorporated data analytics to evaluate the behavior and impacts of the virus. The doctoral research contributed to the scientific body of the knowledge in different ways including (1) presenting a systematic literature review of current research and topics about impacts of the COVID-19 pandemic; (2) predicting the dynamics of the COVID-19 pandemic using deterministic and stochastic Recurrent Neural Networks; (3) predicting the dynamics of the COVID-19 pandemic using graph neural networks; and (4) analyzing the dynamics of the COVID-19 pandemic using graph theoretical method. This dissertation is sorted out as a manuscript-style including four published journal articles. The results of this doctoral research provide a comprehensive view of the behavior and impacts of the COVID-19 pandemic.

Dynamics and growth of many natural and man-made systems can be represented by large-scale complex networks. Entity interactions and community interconnections within complex networks increase the level of difficulty for the investigation on structural network properties such as robustness, vulnerability and resilience. In this dissertation, we develop methodologies based on mixed-integer programming techniques to solve challenging optimization problems that model cascading processes in complex networked systems. In particular, we seek to provide decision making recommendations for problems related to different types of cascading processes in networks commonly considered in a variety of applications: interdependent infrastructure networks and social networks. In the first part, we propose a novel optimization model to enhance the resilience against cascading failure by mitigation and restoration in interdependent networks. We derive a polynomial class of valid inequalities from the cascading constraints and reformulate the substructure that describes capacity restriction to guarantee integral solutions. The computational experiments illustrate that our strengthened formulation outperforms the default setting of a commercial solver on all tested instances. Next, we study the least cost influence maximization problem that arises in social network analytics. We investigate the polyhedral properties of a substructure that is a relaxation of the mixed 0-1 knapsack polyhedron. We give three exponential class of facet-defining inequalities from this substructure and an exact polynomial time separation algorithm for the inequalities. In addition, we propose another new class of strong valid inequalities that dominates the cycle elimination constraints. Through the computational experiments, we demonstrate that a delayed cut generation algorithm that exploits these inequalities is very effective to solve the problem under different settings of network size, density and connectivity.

Eco-industrial parks (EIPs) are promoting a shift from the traditional linear model to the circular model, where Industrial Symbiosis (IS) plays an important role in encouraging the exchange of materials, energy, and waste. The European Directive of ELVs 2000/53/EC considers scrap vehicles- or End-of-Life Vehicles (ELVs)- as waste. According to the official journal of the European Communities, ELVs account for up to 10% of the total amount of waste generated annually in the European Union. This doctoral research incorporated methods and approaches from operations research as well as sustainability science. The research has contributed to the scientific body of the knowledge in the following ways: (1) providing a generalized framework to design Eco-Industrial Parks which serves as a guideline for decision-makers during the first stages of developing EIPs, (2) proposing a design for Eco-Industrial Parks for End-of-Life Vehicles (EIP-4-ELVs) with petrol and diesel types of vehicles, (3) developing a Mixed-Integer Liner Programing (MILP) model which optimizes the exchange of material flows in the network, and (4) presenting a case study of ELVs recovery network in Qatar and apply the developed network. This research is organized as a manuscript-style dissertation including three papers. The results of this research will provide a conceptual model to guide the implementation of Eco-Industrial Parks.