Thesis (Ph. D.)--University of Texas at Austin, 2009. / Title from PDF title page (University of Texas Digital Repository, viewed on Sept. 9, 2009). Vita. Includes bibliographical references.

<p> This dissertation presents an original contribution by defining love as an eco-systemic process with the potential to heal Earth&rsquo;s ecological crisis. Something is considered systemic when it is spread throughout and affects a system as a whole. Considering the view that Earth is an interconnected system, I began to question the role of systemic processes in response to Earth&rsquo;s greater problems, like climate change. A review of the literature revealed that love has not yet been explored as an eco-systemic process in relation to Earth&rsquo;s complex crisis. I chose to address this gap in the literature by engaging a dialogue on the role of love in ecological healing. </p><p> The research is approached through an ecological, or systems, perspective. I developed three methodological tools to assist this inquiry process. The first is what I term the ecological conscience. This could be viewed as the lens of my inquiry and is defined in detail in my methods section. The second is transdisicplinary inquiry, a method of research specifically designed for systems studies. Individual disciplines are beginning to explore the topic of love in more detail&mdash;from the biological reactions of love in the body, to cognitive reactions of interpersonal relationships, to the cultural evolution of love. Each discipline presents a much-needed thread to our understanding of love, but it is important to weave these threads together as a whole. Transdisiciplinary research allowed this process to occur. Finally, I chose storywork methodology as a way to frame my findings on the ecology of love. The story is written as a creative dialogue between myself and the ecology of love and reflects the complexity of my findings in a more personal and emotional tone. </p><p> If something is systemic, its role is crucial to the health of the larger system. That love is appearing in so many disciplines reveals its systemic nature in life. Only by viewing the interconnections can we see how love plays a role in the ecological healing of Earth. This research presents a scientific view of what the poets, saints, and sages have been saying all along. Love matters, and it matters so significantly that its presence or absence influences the evolution of Earth as a whole.</p>

<p> Electricity has become an invaluable commodity for the rest of humanity such that nations irrespective of their classification in the world economy will find it difficult to function without it&rsquo;s reliable supply. For nations such as the United States and the rest of the developed world, sustainable electricity supply is no longer optional. It has become a race for survival and maintenance of the very fabrics of those societies that made them who or what they are. So, whenever there is a disruption of electricity supply due to major natural disasters, the electric utility industry in the United States marshal thousands of first responders. These first responders always answer to the call of duty to face the challenge of restoring this valuable service to affected communities within the shortest possible time. In addition to the human element, electric grid restoration methods after disasters have depended mainly on the ability of intelligent electronic devices (IEDs) to communicate vital grid information with each other for system status. At one end are field devices and at the other end are human operators through outage management systems (OMS) with considerable command and control capabilities using Supervisory Control and Data Acquisition (SCADA) processes. Traditional use of centralized SCADA for system restoration during natural disasters takes too long and presents serious constrains on field workforce especially those on mutual assistance. In this study, we present a hybrid multi agent system (MAS) form of electric grid disaster response management that decentralizes the SCADA functions. The proposed system forms a Mobile Coordination and Restoration Center (MCRC) model that allows the different restoration agents the autonomy to execute restoration functions per outage demand after a disaster. The choice of agent location is modelled on the concept of Facility Location and Relocation Problem &ndash; under Uncertainty (FLRP-U) to identify optimum grid nodes that minimize distance travel and response time for field restoration crews. The model considers a dynamic approach that identifies agent locations based on outage demand changes and minimizes the total weighted distance for first responders. Using systems engineering (SE) concepts, an encompassing viewpoint is presented. The resulting architecture will examine the different agents and subsystems to help establish a technical framework that is logistical for future electric utility disaster response managers. This could be adopted by disaster managers in different settings to achieve improved restoration performance.</p><p>

<p> For decades, the United States (U.S.) Department of Defense (DoD) has developed, deployed, and operated hundreds of different types of systems as components of systems-of-systems. Achieving and maintaining joint systems-of-systems interoperability as new systems are added is a constant problem. The Army, Navy, U.S. Marine Corps, and Air Force each develop requirements, budgets, and acquire, field and operate systems that function as part of joint systems-of-systems. Technology and threats are rapidly evolving. These globally deployed systems and systems-of-systems employed by combatant commanders must be continuously modernized or risk becoming obsolete, resulting in potential mission failure and loss of life. </p><p> Using a wireless telecommunications interface standard case study, this research developed a unique method of planning systems-of-systems modernization using a system dynamics (SD) approach. This approach: a) accounts for key factors that influence the dynamic behavior of systems-of-systems modernization, impacting the ability to modernize systems-of-systems, and b) enables what-if analysis, and decision-making support of systems-of-systems modernization planning options. This research used a mixed-methods approach to demonstrate that the SD model is measurably superior to past practice. Quantitative statistical analysis was performed on 20 years&rsquo; (2001&ndash;2020) of data. A qualitative, scenario-based approach was used to develop an SD model. The results demonstrate that engineers, managers, and senior decision makers in the DoD can realize statistically significant gains by using an SD model to develop and explore systems-of-systems modernization planning options. This research&rsquo;s original contribution to knowledge is the development and validation of an SD model for planning systems-of-systems modernization using a mixed-methods research approach.</p><p>

<p> The Systems Engineering community spends considerable effort developing system specifications during the design phase. Yet during the operational and support phase, there is a potential degradation of those specifications in the form of delayed, missed, or insufficient maintenance (i.e., maintenance that does not restore the system to the design specifications), which are commonly called departures (e.g., Structure/Weld Joint - Incorrect Electrode Usage, Valve Timing, etc.). While the impact of each departure on the system is reviewed as part of the current approval process, there is no evaluation to the equipment and/or personnel from the accumulated number of departures. </p><p> The impact of these accumulated departures is analyzed to determine if there is a correlation between these accumulated departures and casualties to equipment (documented on casualty reports that impact system availability and operational readiness) and/or mishaps to document a safety event and/or damage to property. The analysis required the development of a framework to systematically store and catalog U.S. Navy data on a select set of hulls from 2004 to 2016 specifically addressing data on 6,810 departures, 4,808 casualty reports, and 6 mishaps. A series of analyses were conducted to include (1) ensuring the hulls met the criteria of in-service (i.e., operational ready for deployment), (2) test for trends together as a class followed by this same analysis on a per hull basis, which helped define the correct correlation method, (3) Spearman&rsquo;s rank-order correlation analyses as a class followed by this same analysis on a per hull basis and (4) regression analysis to determine if departures could be used to predict future casualties. The correlations and regression results suggested meaningful, statistically significant at the 0.01 level, for a majority of the relationships between the accumulated number of departures determined at the class level and for each individual hull. The framework and process that is described in this paper can be used to track and influence the number of casualty reports that are predicted to occur by controlling the number of accumulated departure from specifications. There also was no correlation determined between the accumulated departures from specifications and subsequent Mishaps and thus there was no regression analysis conducted.</p><p>

<p> Immature systems can be plagued by high costs, high risks, and schedule delays. System maturity models are utilized to quantitatively analyze a system&rsquo;s readiness to indicate whether the system is prepared to proceed to the next acquisition phase during development. Most system maturity models are not robust enough to properly represent a system and assess its capabilities. Contemporary maturity models focus on individual technologies and fail to portray the importance of the integrations between technologies. System Readiness Level (SRL) was introduced to incorporate integration into a maturity assessment and to allow for a better evaluation of a system. However, adoption of SRL has been halted because of limitations due to the infancy of the model. This study demonstrates that by applying Petri Nets to SRL, a better maturity model is created, called Petri Net SRL (PNSRL). By incorporating Petri Nets, integrations are better represented, permitting a better system view that allows step-wise validation. PNSRL can be used with past SRL mathematical methods, which are simplified and easier to use. These enhancements offered by PNSRL allow an increased decision-making capability for projects. In this study, a capacity PNSRL method is developed to utilize Petri Net place capacities, offering a weighted SRL metric. Finally, PNSRL is verified against six SRL properties, showing that PNSRL is an acceptable maturity model.</p><p>

<p> Systems engineering rigor has been used successfully in the aerospace and defense industries where the development cycles tend to be rather long. However, the practice of using a traditional waterfall, spiral or V-model systems engineering life cycle framework, in other industries brings into question their appropriateness when considering the relative speed of new product development in industrial manufacturing. The purpose of this research is to investigate the applicability of incorporating systems engineering principles in the industrial sector to determine whether there is a statistical association with the overall growth of diversified industrial firms. This research focuses on investigating three systems engineering life cycle approaches: incremental &amp; iterative methods, lean enablers for systems engineering and agile systems engineering; using a semi-structured interviewing approach with subject matter experts from the Fortune 500 diversified industrial sector. The research reveals that there are weak statistical associations between the use of the incremental &amp; iterative and lean systems engineering life cycle approaches when considering the financial growth of the diversified industrial sector. However, the research reveals that there is a strong statistical association between the financial growth of companies in the diversified industrial sector and the use of the agile systems engineering life cycle approach as well as other unique life cycle tools.</p><p>

<p> The resource mix for the U.S. electrical power grid is undergoing rapid change with increased levels of solar photovoltaic (PV) and wind turbine electricity generating capacity. There are potential negative impacts to grid resilience resulting from hurricane damage to wind and solar power stations connected to the power transmission grid. Renewable power sources are exposed to the environment more so than traditional thermal power sources. To our knowledge, damage to power generating stations is not included in studies on hurricane damage to the electrical power grid in the literature. The lack of a hurricane wind damage prediction model for power stations will cause underestimation of predicted hurricane wind damage to the electrical grid with high percentages of total power generation capacity provided by solar photovoltaic and wind turbine power stations. </p><p> Modeling hurricane wind damage to the transmission grid and power stations can predict damage to electrical grid components including power stations, the resultant loss in power generation capacity, and restoration costs for the grid. This Praxis developed models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data were used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study was developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the National Renewable Energy Lab (NREL) Futures Study 2050 Texas scenarios for 50% and 80% renewable energy. </p><p>

<p> The United States economic and national security sectors remain vulnerable to shortages of critical materials due to the risks posed by disruptions in globally-dispersed supply networks. Numerous methods over the past 10 years have been proposed to identify, assess, and evaluate risks in critical material supply chains. This praxis provides a method to quantify the impact of supply disruptions and inform the application of risk mitigation measures for a critical material supply chain from mineral deposits to final platform. It proposes a Bayesian network modeling method not yet applied to the problem in publicly available studies and fits with an assessment methodology proposed by the National Science and Technology Center (NSTC). Results from this study provide indicative answers to how supply disruptions propagate through a selected critical material supply network, which nodes are vulnerable to supply disruptions, and whether mitigating actions can reduce the impact of supply disruptions. The approach here demonstrates that a Bayesian network model can be one of the tools in a criticality assessment methodology.</p><p>

<p> In an effort to control cost and schedule growth, the US Department of Defense mandates that defense acquisition programs perform Technology Readiness Acquisitions (TRAs) during the acquisition cycle. Technology maturity is widely believed to correlate with cost and schedule risk in complex development programs, with the Technology Readiness Level (TRL) the metric currently used for assessing technology maturity. However, while a schedule-related correlation has been demonstrated, no research has shown a statistically significant correlation between a system&rsquo;s overall technology maturity and cost growth. This study demonstrates that an acceptable system level metric can be constructed with the available TRLs that aids in controlling cost growth. This work validates this metric as a useful tool for program managers and system engineering professionals. Utilizing published data on US Department of Defense acquisition programs, this study defines a System Maturity Level (SML) metric that can be computed from existing TRLs and is a statistically significant predictor of cost risk. A System Maturity Level cost-risk curve is also introduced in order to help engineering managers make cost-risk decisions.</p><p>