The Hybrid Electric Vehicle (HEV) is a complex electromechanical system with complex interactions among various components. Due to the large number of design variables involved, the design flexibility in the HEV makes performance studies difficult. As the system complexity and sophistication increases, it becomes much more difficult to predict these interactions and design the system accordingly. Also, different variations in the design and manufacture of various components and systems involve a large amount of work and cost to keep updated of all these variations. While the above issues ask for a flexible design environment suitable for vehicle design, most of the existing powertrain design tools are based on experiential models, such as look-up tables, which use idealized assumptions and limited experimental data. The accuracy of the results produced by these tools is not good enough for designing these new generation vehicles. Also, sometimes the designs may lead to components or systems beyond physical limitations. To make the powertrain design more efficient, the models developed must be closely related to the underlying physics of the components. Only such physics-based models can facilitate high fidelity simulations for dynamics at different time scales. This results in the quest for a design tool that manages the vehicle?s development process while maintaining tight integration between the software and physical artifacts. The thesis addresses the above issues and focuses on the modeling of HEV using model integrated computing and employing physics-based resistive companion form modeling method. For this purpose, Generic Modeling Environment (GME), software developed by Institute of Software and Integrated Systems (ISIS), Vanderbilt University is used as the platform for developing the models. A modeling environment for hybrid vehicle design is prepared and a Battery Electric Vehicle (BEV) is developed as an application of the developed environment. Resistive companion form models of various BEV components are prepared and a model interpreter is prepared for integrating the developed component models and simulating the design.

Throughout the history of software development, advances have been made that improve the ability of developers to create systems by enabling them to work closer to their application domain. These advances have given programmers higher level abstractions with which to reason about problems. A separation of concerns between logic and implementation allows for reuse of components, portability between implementation platforms, and higher productivity. Parallels can be drawn between the challenges that the field of reconfigurable computing (RC) is facing today and what the field of software engineering has gone through in the past. Most RC work is done in low level hardware description languages (HDLs) at the circuit level. A large productivity gap exists between the ability of RC developers and the potential of the technology. The small number of RC experts is not enough to meet the demands for RC applications. Model-based engineering principles provide a way to reason about RC devices at a higher level, allowing for greater productivity, reuse, and portability. Higher level abstractions allow developers to deal with larger and more complex systems. A modeling environment has been developed to aid users in creating models, storing, reusing and generating hardware implementation code for their system. This environment serves as a starting point to apply model-based techniques to the field of RC to tighten the productivity gap. Future work can build on this model-based framework to take advantage of the unique features of reconfigurable devices, optimize their performance, and further open the field to a wider audience. / Master of Science

This study proposes a metamodel, named Federation Architecture Metamodel (FAMM), for describing the architecture of a High Level Architecture (HLA) compliant federation. The metamodel provides a domain specific language and a formal representation for the federation adopting Domain Specific Metamodeling approach to HLA-compliant federations. The metamodel supports the definitions of transformations both as source and as target. Specifically, it supports federate base code generation from a described federate behavior, and it supports transformations from a simulation conceptual model. A salient feature of FAMM is the behavioral description of federates based on live sequence charts (LSCs). It is formulated in metaGME, the meta-metamodel for the Generic Modeling Environment (GME). This thesis discusses specifically the following points: the approach to building the metamodel, metamodel extension from Message Sequence Chart (MSC) to LSC, support for model-based code generation, and action model and domain-specific data model integration. Lastly, this thesis presents, through a series of modeling case studies, the Federation Architecture Modeling Environment (FAME), which is a domain-specific model-building environment provided by GME once FAMM is invoked as the base paradigm.

The High Level Architecture (HLA), IEEE Std. 1516-2000, provides a general framework for distributed modeling and simulation applications, called federations. HLA focuses on interconnection of interacting simulations, called federates, with special emphasis on reusability and interoperability. An HLA object model, be it a simulation object model (SOM), a federation object model (FOM) or the management object model (MOM), describes the data exchanged during federation execution. This thesis introduces a metamodel for the HLA Object Model, fully accounting for IEEE Std. 1516.2. The metamodel is constructed with GME (Generic Modeling Environment), a meta-programmable tool for domain-specific modeling, developed at Vanderbilt University. GME generates a design environment for HLA object models having the HLA OM metamodel as input. This work can be regarded as a step for bringing model-integrated computing to bear on HLA-based distributed simulation.

Novel infectious diseases in humans are of great concern to public health authorities and researchers in epidemiology. Zoonotic pathogens in particular have the potential to cause epidemics without any or little warning. In this thesis, I investigate evolutionary and environmental conditions, and the interactions between both, which facilitate the zoonotic emergence of novel pathogens. I start with a list of the mechanisms and processes which might influence a zoonotic emergence, and identify some unsolved problems. I address these with multiple, theoretical models. First, I use a village-city model with different adaptation scenarios to examine the influence of spatial heterogeneity on the emergence process. I derive general analytical results for the statistical properties of emergence events, including the probability distribution of outbreak sizes. My results suggest that, for typical connection strengths between communities, spatial heterogeneity has only a weak effect on outbreak size distributions, and on the risk of emergence per introduction. Next, I extend the research on environmental conditions by looking at pathogen specialisation in multi-host systems. I derive threshold connectivities for which generalist pathogens, which infect multiple species and might therefore be more dangerous to cross into the human species, can sustain transmission and are not dominated by specialists, which can only cause sustained transmission chains in a single host species, but are able to cause emergences with little warning. My third research chapter is interested in the effect of the loss of biodiversity. I analytically derive expected prevalences for fast growing and slow growing species. If fast growing species tend to perform better in degraded environments, my analytical results suggest that the overall prevalence level of infectious diseases will rise as environments degrade, which facilitates the chance of zoonotic jumps. In my last research chapter, I examine the actual impact of a novel, emerging infectious disease. I use data from the recent `Swine flu' epidemic in England to estimate epidemiological parameters of the infectious agent. My results suggest that the majority of infected cases showed no or only mild symptoms. This reveals that more data than just the estimated number of cases are necessary to fully evaluate the danger of a possible zoonotic, emerging infectious disease. I conclude by discussing my results and the implications which these might have.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis investigates integrating recommender systems into model-driven engineering tools powered by domain-specific modeling languages. The objective of integrating recommender systems into such tools is overcome a shortcoming of proactive modeling where the modeler must inform the model intelligence engine how to progress when it cannot automatically determine the next modeling action to execute (e.g., add, delete, or edit). To evaluate our objective, we integrated a recommender system into the Proactive Modeling Engine, which is a add-on for the Generic Modeling Environment (GME). We then conducted experiments to both subjective and objectively evaluate the enhancements to the Proactive Modeling Engine. The results of our experiments show that integrating recommender system into the Proactive Modeling Engine results in an Average Reciprocal Hit-Rank (ARHR) of 0.871. Likewise, the integration results in System Usability Scale (SUS) rating of 77. Finally, user feedback shows that the integration of the recommender system to the Proactive Modeling Engine increases the usability and learnability of domain-speci c modeling tools.

The Oil Pollution Act of 1990 requires the development of Regional and Area Contingency Plans. For more than 20 years, the State of Florida, under both the Department of Environmental Protection and the Florida Fish and Wildlife Commission, has worked closely with the U.S. Coast Guard and the National Oceanic and Atmospheric Administration to develop these plans for coastal and marine oil spill response. Current plans, developed with local, state and federal stakeholder input, use geographic information systems (GIS) data such as location and extent of sensitive ecological, wildlife, and human-use features (termed Environmental Sensitivity Index data), pre-defined protection priorities, and spatially explicit protection strategies to support decision-making by responders (termed Geographic Response Plans). However, they are long overdue for improvements that incorporate modern oceanographic modeling techniques and integrated data from coastal ocean observing systems. Better understanding of circulation in nearshore and estuarine waters, at a scale consistent with other spatial data, is especially lacking in Area Contingency Plans. This paper identifies the gaps in readily available information on the circulation-driven causes and effects missing in current oil spill contingency planning and describes a sample methodology whereby multiple coastal and ocean spatial science disciplines are used to answer questions that no single, non-integrated discipline can answer by itself. A path forward for further integration and development of more comprehensive plans to better support coastal protection in Florida is proposed. The advances made here are applicable to other coastal regions of the world.