Thesis--University of Wisconsin--Madison. / Typescript. Vita. Description based on print version record. Includes bibliographical references (leaves 273-294).

Thesis (D. Min.)--Trinity International University, 2000. / Abstract. Includes bibliographical references (leaves 104-110).

Thesis (D. Min.)--Concordia Theological Seminary, Ft. Wayne, 1996. / Includes bibliographical references (leaves [17]-[19]).

This study examined the equal opportunity policies utilized by a higher education institution and the effects these policies have on the matriculation patterns and graduation rates of African-American students. The selected policies were: (a) recruitment, (b) admissions, (c) financial aid, and (d) retention. The data were qualitative and included collection of official institutional documents and oral testimonies relating to the subject being investigated. Two research questions were designed to guide the investigation into the link between recruitment, admission, retention, and financial aid policies and enrollment and graduation of African-American students. The findings revealed there was a minor link between recruitment, admissions, financial aid, and retention policies and the number of African-Americans matriculating and graduating from the Georgia Institute of Technology from 1980-1988. It is hoped the findings and implications of this study will contribute to the literature in the field of higher education administration and related fields of knowledge.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Physics, 1961. / Includes bibliographical references (leaf 24). / by Max Johann Keck. / Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Physics, 1961.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 125-135). / Rapidly falling wind and solar energy costs over the past four decades have led to exponential growth in installation of these technologies. However, these intermittent renewables do not reliably produce power on demand. One possible mitigation strategy is the addition of energy storage technologies, which are able to shift generation to later periods of higher demand or price. In competitive markets, storage adoption to facilitate renewables penetration will depend on how much value storage can bring to a wind or solar power plant. Which of the diverse energy storage technologies are best suited to profitably perform this function? How do price and resource variability determine the preferred technologies? This thesis develops two novel methods of comparing storage technologies in hybrid wind-storage or solar-storage power plants. In the first, we evaluate technologies based on the increased value of a marginal hybrid plant under today's conditions. We further explain these results by finding the determinants of storage value under uncertainty. In the second, we find the least-cost hybrid plants able to meet predefined demand profiles. Through simulation, optimization, and statistical analysis, we address the following questions: 1) How can one compare candidate storage technologies? 2) What price and resource features determine storage value? 3) What are the cost targets for storage under different market conditions? To address question 1, we optimize storage operation and size for grid-scale energy arbitrage, and study the value of hybrid plants using different storage technologies. The value of the hybrid plant is found by comparing benefits to costs, and is estimated across locations and technologies. We show that at today's wind and solar generation costs, some storage technologies can provide value, but further cost improvement is needed, especially for electrochemical technologies, to facilitate widespread adoption. Finally, we determine both cost targets and the optimal direction of cost improvement for diverse storage technologies and locations. In order to answer question 2, we identify features of the electricity market and the renewables resource availability that determine value. Through simulations of an artificial price time series in which features of electricity price spikes are varied, we find that storage value is driven by the frequency and amplitude of price spikes and the availability of the energy resource. The durations of price spikes determine the relative value of one storage technology to another, because of differing technology cost structures. We demonstrate these results in historical data and explain the differences in storage value across locations. We also explore how uncertainty in future prices impacts storage value. We determine a new heuristic for storage operation and sizing absent perfect foresight. This approach is able to capture at least 80% of the expected value under perfect foresight and improves upon existing heuristics. In answering question 3, we determine the least-cost combination of wind and solar with storage that provides reliable, dispatchable, pre-determined outputs. This approach allows for the evaluation of storage technologies for a possible future with higher renewables penetration. Preferred technologies for this use context have very low energy capacity costs (< $50/kWh), enabling inexpensive installation of long duration storage. Long periods of low wind or solar availability determine storage requirements and can be mitigated by including both wind and solar in the generation portfolio. New cost targets are derived for storage development that would help enable higher levels of renewables adoption. / by Joshua Michael Mueller. / Ph. D. in Engineering Systems / Ph. D. in Engineering Systems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. "Doctor of Philosophy in Healthcare Systems: Management and Policy Research." / Includes bibliographical references. / For my PhD thesis, I conducted behavioral science research and wrote three first- author journal format papers, of which one paper has been published and the other two will be submitted to healthcare management journals after completion of my degree. All three papers introduce new information about either the cost or the behaviors of patients in local clinics, filling a gap in the healthcare system's management and policy literature. The first paper studies patients with diabetes who are non-adherent to scheduled appointments with physicians in a specialized diabetes clinic setting in Boston. I developed and introduced new and interesting ''technology comfort" measures and a "Smartphone usage" scale, to evaluate if patients would be able to use smart technologies for their disease self-management. This paper not only suggests that patients with diabetes could potentially benefit from using existing advanced technologies, but that new policies can be introduced to reduce the rate of diabetes patients' appointment-related non-adherence. The second paper examines the system of adherence or self-management in five areas ( diet, exercise, medications, doctor's appointments and regular glucose monitoring), revealing how it is correlated to emergency visits and patient lifestyle satisfaction. I analyze predictors of emergency room visits and propose potential policies to reduce these ER visits through the use of advanced smart technologies. The third paper identifies the incidence and consequences of not practicing non- pharmaceutical interventions, during the time of a pandemic, in a student population at a local university clinic. / by Sahar Hashmi, MD. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 241-257). / Electricity tariffs typically charge residential users a volumetric (that is, per-unit of electricity consumed) price that recovers the bulk of the costs of generating, transmitting, and distributing electrical energy. These tariffs also often include taxes and recover other costs associated with regulatory or policy measures. The resulting prices do not reflect the true social marginal costs of generating, transmitting, and distributing energy, capturing little or none of the temporal and geographic variability of marginal electricity costs. These inefficient rates incentivize customers to over-consume power during periods of peak system stress and under-consume power during periods of relatively low demand; this dynamic drives up power system costs, costing Americans and Europeans tens of billions of dollars annually. Critically, it leads to investments in long-lived and low-utilization infrastructure needed to meet peak demands. / Economists have long argued for reforming rates, but progress has historically been slow. Today, less than one quarter of one percent of residential electricity customers in the United States pay a tariff that reflects the real-time price of energy. The emergence of distributed energy resources -- such as solar photovoltaics and battery energy storage -- / has sparked renewed interest among regulators and utilities in reforming electricity tariffs. Efficient rates hold the potential to improve the economic efficiency of distributed energy resource installation and operation decisions. However, the economic pressure to redesign electricity rates is countered by concerns of how more efficient rate structures might impact different socioeconomic groups. In particular, regulators have been dubious of efforts to reform how the costs of network infrastructure (that is, transmission and distribution networks) are recovered, rejecting more than 75% of such efforts in the U.S. in 2017. Focusing on developed power systems in contexts like the U.S. and Europe, this Thesis examines the distributional impacts of rate reform and proposes methods to improve the economic efficiency of rates without creating undesirable distributional impacts. / This Thesis also explores the distributional impacts of rooftop solar photovoltaics adoption under alternative rate designs. This Thesis leverages data on electricity consumption measured half-hourly for more than 100,000 customers in the Chicago, Illinois area, paired with Census data to gain unprecedented insight into the impacts of reforming electricity pricing across customers of varying socioeconomic statuses. This Thesis then builds a simple model of the local utility's -- Commonwealth Edison's -- / cost of service, and simulates solar PV adoption under alternative rate designs, measuring the impacts on customers of differing income levels. This Thesis demonstrates that low-income customers would face increases in expenditures on average in a transition to rates that recover residual network and policy costs through economically efficient fixed charges. However, this Thesis demonstrates that simple changes to fixed charge designs can mitigate these disparities while preserving all, or the vast majority, of the efficiency gains. These designs rely exclusively on observable information and could be replicated by utilities in many geographies across the U.S. / Rooftop solar PV adoption under tariffs with inefficient, volumetric residual cost recovery are shown to create substantial distributional challenges: PV adoption under such tariffs increases expenditures substantially for non-adopters, which tend to be predominately lower income customers; efficient tariffs prevent this regressive cost shifting. In short, failing to reform rates may lead to worse distributional outcomes than reforming rates, even if reforms are implemented naively. Collectively, the findings in this Thesis underscore the need for regulatory reform around electricity pricing, and chart a path forward for balancing economic efficiency and distributional equity in public utility pricing. / by Scott P. Burger. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 139-150). / Networks are essential modeling tools in engineering, business, and public policy research. They can represent physical connections, such as manufacturing processes. They can be relationships among people, such as patient treatment in healthcare. They can also represent abstract interactions, such as the biological reaction between a certain vaccine and a certain virus. In this work, we bring several seemingly disparate problems under the same modeling framework, and show their thematic coherence via the angle of dynamic optimization on networks. Our research problems are drawn from business risk management, public health security, and public policy on vaccine selection. A common theme is the integrative design of (1) strategic resource placement on a network, and (2) operational deployment of such resources. We outline the research questions, challenges, and contributions as follows. / Modern automotive manufacturing networks are complex and global, comprising tens of thousands of parts and thousands of plants and suppliers. Such interconnection leaves the network vulnerable to disruptive events. A good risk mitigation decision support system should be data-driven, interpretable, and computational efficient. We devise such a tool via a linear optimization model, and integrate the model into the native information technology system at Ford Motor Company. In public security, policymakers face decisions regarding the placement of medical resources and training of healthcare personnel, to minimize the social and economic impact of potential large scale bio-terrorism attacks. Such decisions have to integrate the strategic positioning of medical inventories, understanding of adversary's behavior, and operational decisions that involve the deployment and dispensing of medicines. / We formulate a dynamic robust optimization model that addresses this decision question, apply a tractable solution heuristic, and prove theoretical guarantees of the heuristic's performance. Our model is calibrated with publicly available data to generate insights on how the policymakers should balance investment between medical inventory and personnel training. The World Health Organization and regional public health authorities decide on the influenza (flu) vaccine type ahead of flu season every year. Vaccine effectiveness has been limited by the long lead time of vaccine production - during the production period, flu viruses may evolve and vaccines may become less effective. New vaccine technologies, with much shorter production lead times, have gone through clinical trials in recent years. We analyze the question of optimal vaccine selection under both fast and slow production technologies. We formulate the problem as a dynamic distributionally robust optimization model. / Exploiting the network structure and using tools from discrete convex analysis, we prove some structural properties, which leads to informative comparative statics and tractable solution methods. With publicly available data, we quantify the societal benefit of current and future vaccine production technologies. We also explore the reduction in disease burden if WHO expand vaccine portfolio to include more than one vaccine strain per virus subtype. In each of the applications, our main contributions are four-fold. First, we develop mathematical models that capture the decision process. Second, we provide computational technology that can efficiently process these models and generate solutions. Third, we develop theoretical tools that guarantee the performance of these computational technology. Last, we calibrate our models with real data to generate quantitative and implementable insights. / by Peter Yun Zhang. / Ph. D. in Engineering Systems / Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

Thesis: Ph. D., Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 181-187). / Authentic approaches to design education are typically defined as experiences centered on industry involvement. This industry connection is commonly either in the form of projects provided by industry partners or practicing engineers that serve as mentors to students. After exploring the goals and current practices of design education, this dissertation proposes an expanded definition of authentic design education: any design project with impact beyond the classroom environment that encourages the development of a student's self-identity as an engineer. To investigate the potential benefits afforded by an expanded definition of authentic design, a new design class was developed, taught, and evaluated across four years. The class, entitled Design for the Wilderness, was developed with a focus on projects that have impact beyond the classroom environment. Students were required to design and build products that they relied on while traveling in remote wilderness environments. / These impactful projects required students to experience the results of their design decisions. Building on our experiences implementing Design for the Wilderness, a curricular approach of Design for Use is introduced that requires students to use products developed by their peers. Design for Use helps increase students' understanding of human-centered design principles by encouraging students to confront the interplay between their intentions when designing a product and their experiences when failing to understand the intentions behind products designed by their peers. This dissertation also considers a mechanical engineering capstone design class (MIT's 2.009). An interesting outcome of this class is that some teams continue to work on commercializing their products after the semester ends. Team characteristics most strongly correlated with persisting on product development beyond the end of the class are related to healthy team dynamics and a positive social environment. / Teams that persisted spent more of their time working together, had fewer teammates that worked significantly more or less than the team average, and spent more of their time simply "hanging out" in lab. Drawing on our findings from investigating multiple approaches to authentic design education, recommendations are made for the future development of effective engineering design classes. / by Christopher R. Saulnier. / Ph. D. / Ph.D. Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society