18,214 research outputs found

    Building body identities - exploring the world of female bodybuilders

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    This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances

    Towards a sociology of conspiracy theories: An investigation into conspiratorial thinking on D枚nmes

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    This thesis investigates the social and political significance of conspiracy theories, which has been an academically neglected topic despite its historical relevance. The academic literature focuses on the methodology, social significance and political impacts of these theories in a secluded manner and lacks empirical analyses. In response, this research provides a comprehensive theoretical framework for conspiracy theories by considering their methodology, political impacts and social significance in the light of empirical data. Theoretically, the thesis uses Adorno's semi-erudition theory along with Girardian approach. It proposes that conspiracy theories are methodologically semi-erudite narratives, i.e. they are biased in favour of a belief and use reason only to prove it. It suggests that conspiracy theories appear in times of power vacuum and provide semi-erudite cognitive maps that relieve alienation and ontological insecurities of people and groups. In so doing, they enforce social control over their audience due to their essentialist, closed-to-interpretation narratives. In order to verify the theory, the study analyses empirically the social and political significance of conspiracy theories about the D枚nme community in Turkey. The analysis comprises interviews with conspiracy theorists, conspiracy theory readers and political parties, alongside a frame analysis of the popular conspiracy theory books on D枚nmes. These confirm the theoretical framework by showing that the conspiracy theories are fed by the ontological insecurities of Turkish society. Hence, conspiracy theorists, most readers and some political parties respond to their own ontological insecurities and political frustrations through scapegoating D枚nmes. Consequently, this work shows that conspiracy theories are important symptoms of society, which, while relieving ontological insecurities, do not provide politically prolific narratives

    Factors shaping future use and design of academic library space

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    COVID is having immediate and long-term impacts on the use of libraries. But these changes will probably not alter the importance of the academic library as a space. In the decade pre COVID libraries saw a growing number of visits, despite the increasing availability of material digitally. The first part of the paper offers an analysis of the factors driving this growth, such as changing pedagogies, diversification in the student body, new technologies plus tighter estates management. Barriers to change such as academic staff readiness, cost, and slow decision making are also presented. Then, the main body of the paper discusses emerging factors which are likely to further shape the use of library space, namely: concerns with student well-being; sustainability; equality, diversity and inclusion, and decolonisation; increasing co-design with students; and new technologies. A final model captures the inter-related factors shaping use and design of library space post COVID

    Analysis of reliable deployment of TDOA local positioning architectures

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    .Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS

    Interactive Sonic Environments: Sonic artwork via gameplay experience

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    The purpose of this study is to investigate the use of video-game technology in the design and implementation of interactive sonic centric artworks, the purpose of which is to create and contribute to the discourse and understanding of its effectiveness in electro-acoustic composition highlighting the creative process. Key research questions include: How can the language of electro-acoustic music be placed in a new framework derived from videogame aesthetics and technology? What new creative processes need to be considered when using this medium? Moreover, what aspects of 'play' should be considered when designing the systems? The findings of this study assert that composers and sonic art practitioners need little or no coding knowledge to create exciting applications and the myriad of options available to the composer when using video-game technology is limited only by imagination. Through a cyclic process of planning, building, testing and playing these applications the project revealed advantages and unique sonic opportunities in comparison to other sonic art installations. A portfolio of selected original compositions, both fixed and open are presented by the author to complement this study. The commentary serves to place the work in context with other practitioners in the field and to provide compositional approaches that have been taken

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

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    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications

    Dise帽o de un sistema de control y planeamiento de trayectoria coordinado en el tiempo para m煤ltiples robots m贸viles no holon贸micos en presencia de obst谩culos

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    La presente tesis tiene como objetivo dise帽ar un sistema de control y planeamiento de trayectoria coordinado para m煤ltiples robots m贸viles no holon贸micos en mapas con presencia de obst谩culos variados. En esta se simula el control y planeamiento en modelos matem谩ticos de tipo bicicleta. El sistema implementado consiste de tres partes, las cuales son el planeamiento de caminos, el generador de trayectorias y el control de seguimiento de trayectorias. El planeamiento de caminos se dividi贸 en tres partes. En la primera parte se desarroll贸 el planeador local para un robot no holon贸mico, modificando el algoritmo Hybrid A*, de manera que utilice las ecuaciones movimiento circular del m贸vil en vez de las cinem谩ticas. Este algoritmo permite al robot encontrar los caminos que lo llevan de una configuraci贸n de posici贸n y orientaci贸n inicial a una final en mapas con obst谩culos variados. En la segunda parte se agreg贸 al planeador local el planeamiento en el tiempo, combinando a este con el algoritmo de planeamiento de caminos en intervalos seguros (SIPP), el cual permite al robot evadir obst谩culos en el tiempo. Finalmente, en la tercera parte se desarroll贸 el planeador global usando el algoritmo de b煤squeda basada en conflictos (CBS), el cual resuelve los conflictos que se presentan entre los caminos de los m贸viles, imponiendo restricciones en el tiempo en el movimiento de cada uno de ellos. Por otro lado, el generador de trayectorias es desarrollado en una 煤nica parte, en la cual, se plantea la funci贸n de costo a optimizar, se calcula todos los gradientes y se plantea utilizar el algoritmo de descenso de gradiente de forma desacoplada para la optimizaci贸n de trayectoria de cada m贸vil. Mientras que el desarrollo del sistema de control de seguimiento de trayectoria se dividi贸 en dos partes. En la primera se linealiza el modelo matem谩tico por extensi贸n din谩mica para sistemas flatness diferencial y en la segunda parte se desarrolla el controlador LQR de cada m贸vil que permite seguir las trayectorias de referencia deseadas. Al t茅rmino de la tesis se logra el planeamiento, generaci贸n de trayectoria y el control de seguimiento de trayectoria de hasta 10 m贸viles no holon贸micos en mapas con obst谩culos variados, evitando la colisi贸n con los obst谩culos del entorno y la colisi贸n con otros m贸viles durante el planeamiento y la optimizaci贸n de trayectoria. As铆 mismo, se verifica que el planeador es capaz de resolver conflictos en entornos propensos al atasco como mapas tipo T o H
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