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This thesis investigates the evolution of two very different complex systems using network theory. This multi-disciplinary technique is widely used to model and analyse vastly diverse systems of multiple interacting components, and therefore, it is applied in this thesis to study the complexity of the systems. This complexity is rooted in the components’ interactions such that the whole system is more than the sum of all the individual parts. The first novelty in this research is the proposal of a new type of structural perturbation, cluster damage, for measuring another dimension of network robustness. The second novelty is the first application of a community detection method, which uncovers space-independent communities in spatial networks, to airport and linguistic networks. A critical property of complex systems – robustness – is explored within a partial model of the Internet, by demonstrating a novel perturbation strategy based on the iterative removal of clusters. The main contribution of this theoretical case study is the methodology for cluster damage, which has not been investigated by literature on the robustness of complex networks. The model, part of the Internet at the Autonomous System level, only serves as a domain where the novel methodology is demonstrated, and it is chosen because the Internet is known to be robust due to its distributed (non-centralised) nature, even though it is often subjected to large perturbations and failures. The first applied case study is in the field of air transportation. Specifically, it explores the topology and passenger flows of the United States Airport Network (USAN) over two decades. The network model consists of a time-series of six network snapshots for the years 1990, 2000 and 2010, which capture bi-monthly passenger flows among US airports. Since the network is embedded in space, the volume of these flows is naturally affected by spatial proximity, and therefore, a model (recently proposed in the literature) accounting for this phenomenon is used to identify the communities of airports that have particularly high flows among them, given their spatial separation. The second applied case study – in the field of language acquisition – investigates the word co-occurrence network of children, as they develop their linguistic abilities at an early age. Similarly to the previous case study, the network model consists of six children and three discrete developmental stages. These networks are not embedded in physical space, but they are mapped to an artificial semantic space that defines the semantic distance between pairs of words. This novel approach allows for an additional dimension of network information that results in a more complete dataset. Then, community detection identifies groups of words that have particularly high co-occurrence frequency, given their semantic distance. This research highlights the fact that some general techniques from network theory, such as network modelling and analysis, can be successfully applied for the study of diverse systems, while others, such as community detection, need to be tailored for the specific system. However, methods originally developed for one domain may be applied somewhere completely new, as illustrated by the application of spatial community detection to a non-spatial network. This underlines the importance of inter-disciplinary research.

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Target Acquisition Weapons Software (TAWS) Version 3.4 is a joint Tactical Decision Aid (TDA) used to predict performance of electro-optic and electro-magnetic (EM/EO) munitions and navigation systems. TAWS is the USAF and USN mission-planning standard for laser-guided, infrared, and TV munitions and navigation systems TDAs. As TAWS continues to deploy through the mission planning community there is a need to establish a systematic approach to assessing TAWS accuracy. This study was an operational assessment of TAWS Infrared (IR) model performance and consisted of two parts: a comparison of model predictions to pilot observations of IR detection range of a static tank target and an assessment of physical temperature predictions. Limiting factors of this project are similar to those encountered in real world utilization of TAWS mission planning TDAs. This evaluation found TAWS predicted detection ranges and target scene model output were representative forecasts of observed values. The TDA provided a good description of background thermal behavior and highlighted the necessity of careful evaluation of the target scene because of component facet complexity and the geometry of facets exposed to the sensor view. The resulting component analysis illuminated the benefit of focusing new TAWS development on improving the target physical model.

Approved for public release; distribution is unlimited / This thesis investigates the types of competition that exist during the different acquisition phase of a weapon system procurement, and discusses the possible effects that competition has on the acquisition cost of these systems. Also, economic, technical and management variables are presented and discussed that may have a significant impact when considering whether to introduce production competition into a program. Five second sourcing methodologies are presented and discussed along with their relative advantages and disadvantages, and a model is presented which allows for a comparison to be performed between the five second sourcing methods. The proposed acquisition strategy for the Advanced Assault Amphibian Vehicle (AAAV) is presented and analyzed as it concerns design and production competition. An analysis of the AAAV program variables is performed, with accompanying economic analysis, which indicates that a Contractor Teaming strategy would be appropriate for introducing production competition should the decision be made to second source. / http://archive.org/details/evaluationofcomp00corc / Captain, United States Marine Corps

Class of 2007 Abstract / Objectives: To investigate the relationship of price and prevalence of statins when new mediations enter the market and when old medications are withdrawn from the market. Methods: Patients that received a statin were enrolled in the Medicare Current Beneficiary Survey (MCBS) from 1992 to 2002. The overall prevalence of each statin as well as the prevalence of each statin for a patient’s drug coverage (no coverage, Medicaid, Medigap, employer coverage, or other public coverage) were analyzed. Results: The overall prevalence of statin was statistically significant for 1992 versus 2002 (p<0.001). When atorvastatin came to the market towards the end of 1996, there was no difference between simvastatin (p=0.24) and pravastatin (p=0.12) in 1997 versus 1998. Conclusions: There was a difference in the prevalence of statins when atorvastatin entered the market. When cerivastatin left the market, there was a difference in the prevalence of statin use. Atorvastatin became the most prevalent statin by the end of 2002. The price of statins appeared to decrease over time from $39.01 in 1992 to $31.95 in 2002. Also, the year atorvastatin was released the average price of statins increased to $36.57 in 1997.

Knowledge Base Refinement is an area of Machine Learning whose primary goal is the automatic detection and correction of errors in faulty expert system's knowledge bases. A very important feature of a refinement system is the mechanism used to select the refinements to be implemented. Since there are usually different ways to fix a fault, most current Knowledge Base Refinement systems use extensive heuristics to choose one or a few alternative refinements from a set of possible corrections. This approach is justified by the intention of avoiding the computational problems inherent in the generation and testing of multiple refinements. On the other hand, such systems are liable to miss solutions. The opposite approach was adopted by the Knowledge Base Refinement system KRUST which proposed many alternative corrections to refine each wrongly-solved example. Although KRUST demonstrated the feasibility of this approach, the potential of multiple refinement generation could not be fully exploited since the system used a limited set of refinement operators in order to contain the number of alternative fixes generated for each fault, and hence was unable to rectify certain kinds of errors. Additionally, the time taken to produce and test a set of refined knowledge bases was considerable for any non-trivial knowledge base. This thesis presents a major revision of the KRUST system. Like its predecessor, the resulting system, STALKER, proposes many alternative refinements to correct each wrongly classified example in the training set. Two enhancements have been made: the class of errors handled by KRUST has been augmented through the introduction of inductive refinement operators; the testing phase of Knowledge Base Refinement has been speeded up considerably by means of a technique based on a Truth Maintenance System (TMS). The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time, STALKER is very efficient since KRUST's computationally expensive implementation and testing of refined knowledge bases has been replaced by a TMS-based simulator.