Design and Development of Geographical Information System (GIS) Map for Nuclear Waste Streams

A nuclear waste stream is the complete flow of waste material from origin to treatment facility to final disposal. The objective of this study was to design and develop a Geographic Information Systems (GIS) module using Google Application Programming Interface (API) for better visualization of nuclear waste streams that will identify and display various nuclear waste stream parameters. A proper display of parameters would enable managers at Department of Energy waste sites to visualize information for proper planning of waste transport. The study also developed an algorithm using quadratic Bézier curve to make the map more understandable and usable. Microsoft Visual Studio 2012 and Microsoft SQL Server 2012 were used for the implementation of the project. The study has shown that the combination of several technologies can successfully provide dynamic mapping functionality. Future work should explore various Google Maps API functionalities to further enhance the visualization of nuclear waste streams.

Natural Disasters and Comparative State-Formation and Nation-Building: Earthquakes in Argentina and Chile (1822-1939)

Natural disasters in Argentina and Chile played a significant role in the state-formation and nation-building process (1822-1939). This dissertation explores state and society responses to earthquakes by studying public and private relief efforts reconstruction plans, crime and disorder, religious interpretations of catastrophes, national and transnational cultures of disaster, science and technology, and popular politics. Although Argentina and Chile share a political border and geological boundary, the two countries provide contrasting examples of state formation. Most disaster relief and reconstruction efforts emanated from the centralized Chilean state in Santiago. In Argentina, provincial officials made the majority of decisions in a catastrophe’s aftermath. Patriotic citizens raised money and collected clothing for survivors that helped to weave divergent regions together into a nation. The shared experience of earthquakes in all regions of Chile created a national disaster culture. Similarly, common disaster experiences, reciprocal relief efforts, and aid commissions linked Chileans with Western Argentine societies and generated a transnational disaster culture. Political leaders viewed reconstruction as opportunities to implement their visions for the nation on the urban landscape. These rebuilding projects threatened existing social hierarchies and often failed to come to fruition. Rebuilding brought new technologies from Europe to the Southern Cone. New building materials and systems, however, had to be adapted to the South American economic and natural environment. In a catastrophe’s aftermath, newspapers projected images of disorder and the authorities feared lawlessness and social unrest. Judicial and criminal records, however, show that crime often decreased after a disaster. Finally, nineteenth-century earthquakes heightened antagonism and conflict between the Catholic Church and the state. Conservative clergy asserted that disasters were divine punishments for the state’s anti-clerical measures and later railed against scientific explanations of earthquakes.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.