Metal music as critical dystopia : humans, technology and the future in 1990s science fiction metal

Metal Music as Critical Dystopia: Humans, Technology and the Future in 1990s Science Fiction Metal seeks to demonstrate that the dystopian elements in metal music are not merely or necessarily a sonic celebration of disaster. Rather, metal music's fascination with dystopian imagery is often critical in intent, borrowing themes and imagery from other literary and cinematic traditions in an effort to express a form of social commentary. The artists and musical works examined in this thesis maintain strong ties with the science fiction genre, in particular, and tum to science fiction conventions in order to examine the long-term implications of humanity's complex relationship with advanced technology. Situating metal's engagements with science fiction in relation to a broader practice of blending science fiction and popular music and to the technophobic tradition in writing and film, this thesis analyzes the works of two science fiction metal bands, VOlvod and Fear Factory, and provides close readings of four futuristic albums from the mid to late 1990s that address humanity's relationship with advanced technology in musical and visual imagery as well as lyrics. These recorded texts, described here as cyber metal for their preoccupation with technology in subject matter and in sound, represent prime examples of the critical dystopia in metal music. While these albums identify contemporary problems as the root bf devastation yet to come, their musical narratives leave room for the possibility of hope , allowing for the chance that dystopia is not our inevitable future.

Medicalization and women : O, the Oprah Magazine diagnosed through critical discourse analysis

This thesis uses critical discourse analysis (COAl to explore and examine direct-toconsumer (OTC) pharmaceutical drug advertisements appearing in four issues of 0, The Oprah Magazine in 2006. The theoretical underpinnings of this thesis emerge from social scientists and feminists analyses regarding the medicalization of everyday life. The findings of this study highlight three types of discourses used by pharmaceutical companies. First, I explore the use of historical and contemporary gender norms to seJi pharmacological products; second, J examine discourses which normalize the use of chemical solutions as the first line of defense to address a wide range of everyday problems; and finally, I assess how phannaceutical advertisements provide an illusion of autonomy by responsibilizing individuals as patients, at the same time as they suggest that real independence can only be achieved with medication. My discussion of these themes also includes an analysis of why 0 Magazine, which explicitly promotes women's empowerment through holistic approaches to health and personal growthmight support such advertising. Thus I explore: how does OTC advertising benefit both pharmaceutical companies and 0 Magazine itself? I conclude through a brief discussion of the larger implications of OTC advertising for women's health.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.