This study developed and analyzed the use of a live virtual constructive (LVC) framework capable of simulating ionizing radiation damage to Unmanned Aerial Vehicles (UAV) during a nuclear power plant disaster. UAV response promises greater safety to humans over helicopters as well as provides longer survivability in the presence of irradiated environments. However, electronics in unmanned systems are subject to radiation damage and over time eventual failure. A LVC simulation framework may offer an independent and low-cost assessment of equipment life expectancy. Knowing life expectancy of equipment for operational scenarios is critical for emergency management planners. This research creates an enhanced RASCAL-LVC simulation framework by modeling and simulating NPP disaster radiation release based on the NRC RASCAL simulation and radioactive cloud dispersion in STAGE. The resulting framework enables analysis of length of operational survivability of UAV electronics for three illustrative missions. The three scenarios examined are: (1) an In-And-Out Mission that simulates Parts Delivery, Surveillance, or passenger pickup/delivery; (2) a Fukushima-like Spent Fuel Pool water replenishment mission with radiation hot spot; and (3) an exploratory Chernobyl-magnitude Reactor Fire-extinguishing Mission with an open reactor radiation hot spot. More generally, the enhanced RASCAL-LVC framework is capable of: (1) supporting human-in-the-loop training and mission rehearsal; (2) design and analysis of a broad spectrum of NPP disaster scenarios and mission responses; (3) analysis of various response vehicles within mission-scenario combinations; and (4) system engineering support to each system's life cycle.

In this decade, with the rise of data science accompanying the growth of e-commerce, many technologies have been developed. An example of these technologies is Blockchain, which has appeared to overcome security problems potentially. This research assesses Blockchain's implementation in supply chains through a methodology that uses deep learning and agent-based simulation. A case study was utilized to observe and validate research developments. The unique method predicts intrusions by using deep learning, and agent-based modeling reproduces artificial but convincing agents (e.g., customers, companies, hackers, and cyber pirates) in a computer-generated market. Trust and other relationships are systematically captured to represent Blockchain additions. Once again, the agent-based simulation model's environment permits hypothetical interactions and emergent features by coordinating supply and demand for business-to-consumer e-commerce events. The case study based on a real environment shows that the proposed method can determine the feasibility of the business model and Blockchain implementation's potential contributions.

In today's world, the healthcare sector is facing challenges to improve the efficiency and effectiveness of its operations. More and more improvement projects are being adopted to enhance healthcare services, making it more patient-centric, and enabling better cost control. Healthcare organizations strive to identify and carry out such improvement initiatives to sustain their businesses and gain competitive advantage. Seeking to reach a higher operational level of excellence, healthcare organizations utilize business excellence criteria to conduct assessment and identify organizational strengths and weaknesses. However, while such assessments routinely identify numerous areas for potential improvement, it is not feasible to conduct all improvement projects simultaneously due to limitations in time, capital, and personnel, as well as conflict with other organization's projects or strategic objectives. An effective prioritization and selection approach is valuable in that it can assist the organization to optimize its available resources and outcomes. This study attempts to enable such an approach by developing a framework to prioritize improvement opportunities in healthcare in the context of the business excellence model through the integration of the Fuzzy Delphi Method and Fuzzy Interface System. To carry out the evaluation process, the framework consists of two phases. The first phase utilizes Fuzzy Delphi Method to identify the most significant factors that should be considered in healthcare for electing the improvement projects. The FDM is employed to handle the subjectivity of human assessment. The research identifies potential factors for evaluating projects, then utilizes FDM to capture expertise knowledge. The first round in FDM is intended to validate the identified list of factors from experts; which includes collecting additional factors from experts that the literature might have overlooked. When an acceptable level of consensus has been reached, a second round is conducted to obtain experts' and other related stakeholders' opinions on the appropriate weight of each factor's importance. Finally, FDM analyses eliminate or retain the criteria to produce a final list of critical factors to select improvement projects. The second phase in the framework attempts to prioritize improvement initiatives using the Hierarchical Fuzzy Interface System. The Fuzzy Interface System combines the experts' ratings for each improvement opportunity with respect to the factors deemed critical to compute the priority index. In the process of calculating the priority index, the framework allows the estimation of other intermediate indices including: social, financial impact, strategical, operational feasibility, and managerial indices. These indices bring an insight into the improvement opportunities with respect to each framework's dimensions. The framework allows for a reduction of the bias in the assessment by developing a knowledge based on the perspectives of multiple experts.

Over 60% of US adults are overweight or obese. Sedentary lifestyles are considered major contributors to the high rates and increasing prevalence of obesity. Physical activity is a critical component in shifting from sedentary lifestyles. Studies indicate that less than half of U.S. adults meet the CDC/ACSM physical activity recommendations. Interactive video games can increase PA, but no study has yet assessed physiologic effort, hedonics, and perceived exertion for playing immersive virtual reality (VR) and controller-free screen-based active video games (AVGs), compared to treadmill walking and resting. We ran 25 subjects (9 female, 16 male) in 10-minute sessions of five conditions. Head Mounted Display VR: Oculus (Fruit Ninja and Boxing), Screen-based AVG: Kinect (Fruit Ninja and Boxing), and Treadmill walking at 3 mph. One, six-condition (Rest, Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA was used to examine differences in HRmean. Three, five-condition (Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA were used to examine differences in HRpeak, ratings of perceived exertion (RPE) and Hedonics (Liking). Post hoc analyses using pairwise comparisons were used to further assess significant main effects of the condition. A Pearson's product-moment correlation was run to assess the relationship between activity condition HRmean and RPE VR Boxing elicited the greatest physiological effort, producing vigorous-intensity PA. There was no significant difference in average heart rate for the Treadmill, Kinect Fruit Ninja, Kinect Boxing, and VR Fruit Ninja. Thus, the Kinect and VR sport and casual games are comparable to treadmill walking PA levels and qualify as moderate-intensity activity. The VR Fruit Ninja, VR Boxing, Kinect Fruit Ninja were the most enjoyed activities. Despite having the highest Heart rate and the highest self-reported Rating of Perceived Exertion (RPE), VR Boxing was significantly more enjoyable than Treadmill Walking. There was no statistically significant correlation between Activity Condition HRmean and RPE. Both casual and sports VR and AVG activities are enjoyable activities for adults, stimulating moderate-to-vigorous activity through a traditionally sedentary medium. This research extends previous works in active video gaming effects on physiological cost, perceived exertion and hedonics and fills the gap relating virtual reality active video games. The significance of the research outcomes is that this analysis provides a scientifically validated approach to support the establishment of physical activity level goals and guidelines in the development of active video games as a response and/or remedy to address the sedentary lifestyles that are contributing to American and global obesity.

Engineering Analytics (EA) is a technique used to derive meaningful insight from gathered data. It is an approach that has arisen, and it includes the process of analyzing data using analytics tools from fields such as Big Data, Machine Learning (ML), traditional operations research, statistics, and numerical methods. Industrial Engineering is an engineering field concerned with how to design better, improve, and install integrated systems, uses EA to understand and continually improve, innovate, and build new processes. Therefore, EA and Virtual Reality (VR) technology can be used in combination with Electroencephalography (EEG), a physiological measurement, to investigate human areas. The objective of this study was to use EA, VR, and EEG to provide insights into the way we study brain attention, simulation sickness, and verbal-visual ability. In this research study, participants were examined in 3D virtual environments by collecting subjective responses as well as recording and analyzing participants' brainwaves. EA techniques were utilized to investigate and discover relationships.

Rail transport has played a significant role for a long time in the U.S. history. As other alternative transport modes have been developed, rail transport has increasingly faced more challenges to stay competitive. While heavy use of the rail, shortened service life and the need to meet consumer expectations have placed challenges, maintenance has become an integral part of the railway industry to assure efficacy and reliability of rail transportation. For this reason, there is a constant need to improve maintenance for a number of purposes, such as safety, quality, capacity, reliability, availability, punctuality, etc. In addition, optimizing maintenance strategy is also contributing to a reduction in management costs and track maintenance and renewal costs, which are extremely expensive cost elements. It also contributes significantly to increasing the life of track components. This research starts with a thorough review of how reliability-centered maintenance (RCM) approaches are applied to rail transportation, their results, analysis, and summary of benefits and limitations of the applied method in railway transport. The objective of this research is to propose recommendations for best practices to improve maintenance and RCM plan with respect to maintenance and operational costs, technology and supporting systems, component life cycle and other related aspects to be considered when designing a maintenance strategy. This research outlines all of the planning processes involved in RCM so that it can be applied comprehensively to match the objectives of different organizations.

Considering a single prediction task at a time is the most commonly paradigm in machine learning practice. This methodology, however, ignores the potentially relevant information that might be available in other related tasks in the same domain. This becomes even more critical where facing the lack of a sufficient amount of data in a prediction task of an individual subject may lead to deteriorated generalization performance. In such cases, learning multiple related tasks together might offer a better performance by allowing tasks to leverage information from each other. Multi-Task Learning (MTL) is a machine learning framework, which learns multiple related tasks simultaneously to overcome data scarcity limitations of Single Task Learning (STL), and therefore, it results in an improved performance. Although MTL has been actively investigated by the machine learning community, there are only a few studies examining the theoretical justification of this learning framework. The focus of previous studies is on providing learning guarantees in the form of generalization error bounds. The study of generalization bounds is considered as an important problem in machine learning, and, more specifically, in statistical learning theory. This importance is twofold: (1) generalization bounds provide an upper-tail confidence interval for the true risk of a learning algorithm the latter of which cannot be precisely calculated due to its dependency to some unknown distribution P from which the data are drawn, (2) this type of bounds can also be employed as model selection tools, which lead to identifying more accurate learning models. The generalization error bounds are typically expressed in terms of the empirical risk of the learning hypothesis along with a complexity measure of that hypothesis. Although different complexity measures can be used in deriving error bounds, Rademacher complexity has received considerable attention in recent years, due to its superiority to other complexity measures. In fact, Rademacher complexity can potentially lead to tighter error bounds compared to the ones obtained by other complexity measures. However, one shortcoming of the general notion of Rademacher complexity is that it provides a global complexity estimate of the learning hypothesis space, which does not take into consideration the fact that learning algorithms, by design, select functions belonging to a more favorable subset of this space and, therefore, they yield better performing models than the worst case. To overcome the limitation of global Rademacher complexity, a more nuanced notion of Rademacher complexity, the so-called local Rademacher complexity, has been considered, which leads to sharper learning bounds, and as such, compared to its global counterpart, guarantees faster convergence rates in terms of number of samples. Also, considering the fact that locally-derived bounds are expected to be tighter than globally-derived ones, they can motivate better (more accurate) model selection algorithms. While the previous MTL studies provide generalization bounds based on some other complexity measures, in this dissertation, we prove excess risk bounds for some popular kernel-based MTL hypothesis spaces based on the Local Rademacher Complexity (LRC) of those hypotheses. We show that these local bounds have faster convergence rates compared to the previous Global Rademacher Complexity (GRC)-based bounds. We then use our LRC-based MTL bounds to design a new kernel-based MTL model, which enjoys strong learning guarantees. Moreover, we develop an optimization algorithm to solve our new MTL formulation. Finally, we run simulations on experimental data that compare our MTL model to some classical Multi-Task Multiple Kernel Learning (MT-MKL) models designed based on the GRCs. Since the local Rademacher complexities are expected to be tighter than the global ones, our new model is also expected to exhibit better performance compared to the GRC-based models.

In the healthcare sector, providing high-quality service in a safe environment for both patient and staff is an obvious and ultimate major objective. Training is an essential component for achieving this important objective. Most organizations acknowledge that employee simulation-based training programs are an important part of the human capital strategy, yet few have effectively succeeded in quantifying the real and precise ROI of this type of investment. Therefore, if the training is perceived as a waste of resources and its ROI is not clearly recognized, it will be the first option to cut when the budget cut is needed. The various intangible benefits of healthcare simulation-based training are very difficult to quantify. In addition, there was not a unified way to count for the different cost and benefits to provide a justifiable ROI. Quantifying the qualitative and intangible benefits of medical training simulator needed a framework that helps to identify and convert qualitative and intangible benefits into monetary value so it can be considered in the ROI evaluation. This research is a response to the highlighted importance of developing a comprehensive framework that has the capability to take into consideration the wide range of benefits that simulation-based training can bring to the healthcare system taking into consideration the characteristics of this specific field of investment. The major characteristics of investment in this field include the uncertainty, the qualitative nature of the major benefits, and the diversity and the wide range of applications. This comprehensive framework is an integration of several methodologies and tools. It consists of three parts. The first part of the framework is the benefits and cost structure, which pays special attention to the qualitative and intangible benefits by considering the Value Measurement methodology (VMM) and other previously existing models. The second part of the framework is important to deal with the uncertainty associated with this type of investment. Monte Carlo simulation is a tool that considered multiple scenarios of input sets instead of a single set of inputs. The third part of the framework considers an advanced value analysis of the investment. It goes beyond the discounted cash flow (DCF) methodologies like net present value (NPV) that consider a single scenario for the cash flow to Real Options Analysis that consider the flexibility over the lifetime of the investment when evaluating the value of the investment. This framework has been validated through case studies.

Safety is an essential issue for organizations to survive, especially for hazardous industries such as the construction industry. The construction industry is considered to be one of the major industries that help in the growth of the economy and the infrastructure of all countries. Recently, scholars have paid increasing attention to the concept of safety culture due to its role in decreasing the occurrences of accidents and injuries. Safety culture has become the focus of all industries and has received much attention in recent years, especially within the construction industry. Absence of this culture is a major cause of injuries and accidents in the construction field. In the construction industry, personnel's perception of safety culture is vital to prevent accidents or behavior misconduct. Also, focusing on personnel's safety culture on construction sites provides an opportunity to decrease risks and unsafe behaviors to improve the overall safety level. Workers' performance and behaviors are shaped by their awareness and view of safety culture inside their work environment. Generally, safety performance in the construction field is still unsatisfactory based on reporting records. The present study observed the influence of safety culture on construction's personnel's safety performance on large governmental construction projects in Saudi Arabia. Construction personnel's safety performance is measured by their attitude toward violations and error behaviors. This research also exams the role of personnel's motivation toward construction safety as a mediating variable between construction safety culture and safety performance constructs, including error and violation behaviors. The research adopted a quantitative method by using a questionnaire for the purpose of data collection and analysis. A total of 434 questionnaires were collected from construction personnel including project managers, engineers, and supervisors through their voluntary participation in this study. Statistical analysis was used to analyze the data collected including descriptive statistics, confirmatory factor analysis (CFA) and structural equation modeling (SEM) techniques. Confirmatory factor analysis is used for validating each factor with its measurable items. Finally, this study applied the concept of structural equation modeling (SEM) to evaluate the correlation between all latent variables in the study's conceptualized model. The outcomes of the study show that safety culture has a direct influence on construction personnel's attitudes toward violations and an indirect effect on construction personnel's error behavior. Furthermore, safety culture has a significant effect on improving safety motivation, as well. Safety motivation for construction safety has a direct effect on errors behaviors. Conversely, safety motivation does not have a mediating effect on construction personnel's attitudes toward violations. Therefore, safety motivation's mediating role was significant only between safety culture and errors behaviors. This research has added to the existing knowledge about the important part of safety culture as a key interpreter of safety performance in construction field. The current study contributes to psychological safety through examining the influence of safety culture as the interpreter for enhancing motivation for construction safety. Additionally, this research evaluated safety culture's influence on construction personnel's attitudes toward violations and construction personnel's error behavior. The outcomes of the study are useful and recommended to be used by construction management to better pinpoint the reasons for unsafe behaviors within the construction industry. The results of this research highlights management's role in determining, and affecting, workers' behaviors.

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled "walk-in" patient visits. The simulation model captures the complex characteristics of the Orlando Veteran's Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint.