Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

Essays on Financial Constraints, R&D Investments, and Competition

This dissertation consists of two chapters of theoretical studies that investigate the effect of financial constraints and market competition on research and development (R&D) investments. In the first chapter, I explore the impact of financial constraints on two different types of R&D investments. In the second chapter, I examine the impact of market competition on the relationship between financial constraints and R&D investments. In the first chapter, I develop a dynamic monopoly model to study a firm’s R&D strategy. Contrary to intuition, I show that a financially constrained firm may invest more aggressively in R&D projects than an unconstrained firm. Financial constraints introduce a risk that a firm may run out of money before its project bears fruit, which leads to involuntary termination on an otherwise positive-NPV project. For a company that relies on cash flow from assets in place to keep its R&D project alive, early success can be relatively important. I find that when the discovery process can be expedited by heavier investment (“accelerable” projects), a financially constrained company may find it optimal to “over”-invest in order to raise the probability of project survival. The over-investment will not happen if the project is only “scalable” (investment scales up payoffs). The model generates several testable implications regarding over-investment and project values. In the second chapter, I study the effects of competition on R&D investments in a duopoly framework. Using a homogeneous duopoly model where two unconstrained firms compete head to head in an R&D race, I find that competition has no effect on R&D investment if the project is not accelerable, and the competing firms are not constrained. In a heterogeneous duopoly model where a financially constrained firm competes against an unconstrained firm, I discover interesting strategic interactions that lead to preemption by the constrained firm in equilibrium. The unconstrained competitor responds to its constrained rival’s investment in an inverted-U shape fashion. When the constrained competitor has high cash flow risk, it accelerates the innovation in equilibrium, while the unconstrained firm invests less aggressively and waits for its rival to quit the race due to shortage of funds.

THERMAL VACUUM TEST CORRELATION OF A ZERO PROPELLANT LOAD CASE THERMAL CAPACITANCE PROPELLANT GAUGING ANALYTICAL MODEL

This thesis describes the development and correlation of a thermal model that forms the foundation of a thermal capacitance spacecraft propellant load estimator. Specific details of creating the thermal model for the diaphragm propellant tank used on NASA’s Magnetospheric Multiscale spacecraft using ANSYS and the correlation process implemented are presented. The thermal model was correlated to within +/- 3 Celsius of the thermal vacuum test data, and was determined sufficient to make future propellant predictions on MMS. The model was also found to be relatively sensitive to uncertainties in applied heat flux and mass knowledge of the tank. More work is needed to improve temperature predictions in the upper hemisphere of the propellant tank where predictions were found to be 2-2.5 Celsius lower than the test data. A road map for applying the model to predict propellant loads on the actual MMS spacecraft in 2017-2018 is also presented.

TRAFFIC IMPACT ANALYSIS OF SEVERAL DYNAMIC LANE MANAGEMENT STRATEGIES FOR CONGESTION MITIGATION BASED ON DTA MODEL

Persistent daily congestion has been increasing in recent years, particularly along major corridors during selected periods in the mornings and evenings. On certain segments, these roadways are often at or near capacity. However, a conventional Predefined control strategy did not fit the demands that changed over time, making it necessary to implement the various dynamical lane management strategies discussed in this thesis. Those strategies include hard shoulder running, reversible HOV lanes, dynamic tolls and variable speed limit. A mesoscopic agent-based DTA model is used to simulate different strategies and scenarios. From the analyses, all strategies aim to mitigate congestion in terms of the average speed and average density. The largest improvement can be found in hard shoulder running and reversible HOV lanes while the other two provide more stable traffic. In terms of average speed and travel time, hard shoulder running is the most congested strategy for I-270 to help relieve the traffic pressure.