Multi-media filtration for removal of petroleum contamination

A pilot scale multi-media filtration system was used to evaluate the effectiveness of filtration in removing petroleum hydrocarbons from a source water contaminated with diesel fuel. Source water was artificially prepared by mixing bentonite clay and tap water to produce a turbidity range of 10-15 NTU. Diesel fuel concentrations of 150 ppm or 750 ppm were used to contaminate the source water. The coagulants used included Cat Floc K-10 and Cat Floc T-2. The experimental phase was conducted under direct filtration conditions at constant head and constant rate filtration at 8.0 gpm. Filtration experiments were run until the filter reached its clogging point as noted by a measured peak pressure loss of 10 psi. The experimental variables include type of coagulant, oil concentration and source water. Filtration results were evaluated based on turbidity removal and petroleum hydrocarbon (PHC) removal efficiency as measured by gas chromatography. Experiments indicated that clogging was controlled by the clay loading on the filter and that inadequate destabilization of the contaminated water by the coagulant limited the PHC removal. ^

Gender, identity, and the security state: The politics of international drug trafficking

This dissertation examined how United States illicit drug control policy, often commonly referred to as the "war on drugs," contributes to the reproduction of gendered and racialized social relations. Specifically, it analyzed the identity producing practices of United States illicit drug control policy as it relates to the construction of U.S. identities. ^ Drawing on the theoretical contributions of feminist postpositivists, three cases of illicit drug policy practice were discussed. In the first case, discourse analysis was employed to examine recent debates (1986-2005) in U.S. Congressional Hearings about the proper understanding of the illicit drug "threat." The analysis showed how competing policy positions are tied to differing understandings of proper masculinity and the role of policymakers as protectors of the national interest. Utilizing critical visual methodologies, the second case examined a public service media campaign circulated by the Office of National Drug Control Policy that tied the "war on drugs" with another security concern in the U.S., the "war on terror." This case demonstrated how the media campaign uses messages about race, masculinity, and femininity to produce privileged notions of state identity and proper citizenship. The third case examined the gendered politics of drug interdiction at the U.S. border. Using qualitative research methodologies including semi-structured interviews and participant observation, it examined how gender is produced through drug interdiction at border sites like Miami International Airport. By paying attention to the discourse that circulates about women drug couriers, it showed how gender is normalized in a national security setting. ^ What this dissertation found is that illicit drug control policy takes the form it does because of the politics of gender and racial identity and that, as a result, illicit drug policy is implicated in the reproduction of gender and racial inequities. It concluded that a more socially conscious and successful illicit drug policy requires an awareness of the gendered and racialized assumptions that inform and shape policy practices.^

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.