We present a dynamical model of the high mass X-ray binary LMC X-1 based on high-resolution optical spectroscopy and extensive optical and nearinfrared photometry. From our new optical data we find an orbital period of P = 3.90917±0.00005 days. We present a refined analysis of the All Sky Monitor data from RXTE and find an X-ray period of P = 3.9094 ± 0.0008 days, which is consistent with the optical period. A simple model of Thomson scattering in the stellar wind can account for the modulation seen in the X-ray light curves. The V − K color of the star (1.17 ± 0.05) implies A V = 2.28 ± 0.06, which is much larger than previously assumed. For the secondary star, we measure a radius of R 2 = 17.0±0.8 R ⊙ and a projected rotational velocity of V rot sin i = 129.9±2.2 km s −1 . Using these measured properties to constrain the dynamical model, we find an inclination of i = 36.38±1.92 • , a secondary star mass of M 2 = 31.79±3.48 M ⊙ , and a black hole mass of 10.91 ± 1.41 M ⊙ . The present location of the secondary star in a temperature-luminosity diagram is consistent with that of a star with an initial mass of 35 M ⊙ that is 5 Myr past the zero-age main sequence. The star nearly fills its Roche lobe (≈ 90% or more), and owing to the rapid change in radius with time in its present evolutionary state, it will encounter its Roche lobe and begin rapid and possibly unstable mass transfer on a timescale of a few hundred thousand years.

The government acquisition system is consistently plagued by cost growth and by attempts at acquisition reform. Despite these persistent challenges, the academic community lacks a methodology for studying complex acquisition programs both in‐depth and longitudinally throughout their life cycles. In this paper, we present a framework for studying complex acquisition programs that provides researchers with a strategy for systematically studying cost growth mechanisms. The proposed framework provides a means to identify specific technical and organizational mechanisms for cost growth, to organize those mechanisms using design structure matrices, and to observe the evolution of those mechanisms throughout a program's life cycle. To illustrate the utility of our framework, we apply it to analyze a case study of the National Polar‐orbiting Operational Environmental Satellite System (NPOESS) program. Ultimately, we demonstrate that the framework enables us to generate unique insights into the mechanisms that induced cost growth on NPOESS and that were unacknowledged by previous studies. Specifically, we observed that complexity was injected into the technical system well before the program's cost estimates began to increase and that it was the complexity of the NPOESS organization that hindered the program's ability to effectively estimate and to manage its costs.

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The term jointness refers to activities or operations that are executed collaboratively by more than one government agency or military department. While joint operations have become increasingly common and successful, the government continues to struggle with joint system acquisition: in fact, although a common motivation for joint acquisition is cost savings, recent studies suggest that joint programs experience larger cost growth than non-joint programs and that it may be more cost effective for agencies to acquire systems independently rather than jointly. This thesis explains why joint programs often experience large cost growth and how jointness itself may induce it.To understand the cost of jointness, this thesis proposes and demonstrates a new approach for studying large, complex acquisition programs whereby the evolution of a program's organizational and technical architectures is quantified and observed using a design structure matrix (DSM)-based tool. Using this approach, one is able to gain an in-depth understanding of the underlying mechanisms that drive a program's costs, as well as global perspective on cost growth throughout a program's lifecycle. The utility of this approach is demonstrated by applying it to study the cost impacts of jointness on three programs that developed environmental monitoring systems for low Earth orbit.

The demand for renewable and sustainable energy has generated considerable interest in the conversion of cellulosic biomass into liquid fuels such as ethanol using a filamentous fungus. While attempts have been made to study cellulose metabolism through the use of knock-out mutants, there have been no systematic effort to characterize natural variation for cellulose metabolism in ecotypes adapted to different habitats. Here, we characterized natural variation in saccharification of cellulose and fermentation in 73 ecotypes and 89 laboratory strains of the model fungus Neurospora crassa. We observed significant variation in both traits among natural and laboratory generated populations, with some elite strains performing better than the reference strain. In the F1 population N345, 15% of the population outperformed both parents with the top performing strain having 10% improvement in ethanol production. These results suggest that natural alleles can be exploited through fungal breeding for developing elite industrial strains for bioethanol production.