1,628 research outputs found

    Tracing cultural change in the reproduction of intolerance : 'secularism', 'Islamism' and others in Turkey’s experience of democratization

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    Defence date: 16 January 2020Examining Board: Ayhan Kaya, Istanbul Bilgi University; Hanspeter Kriesi, European University Institute – SPS Department; Élise Massicard, CERI, Sciences Po; Olivier Roy, European University Institute – SPS Department (Supervisor)How do cultural resources such as values and beliefs, and their functions in ideology-making, change? In the democratization literature, the value-based approach to culture seeks cultural change based on values. However, the combination of this approach with value-surveys fails to consider several ways in which change may unfold between cultural periods. Instead, this study will delve into a history of conversational texts, which are endogenously grounded within culture, capable of demonstrating culture in action and reflecting what is collective about culture as it operates through dialectical encounters. I focus on change in three landscapes of culture in Turkey, which have witnessed some of the most persistent stories of the unequal relationship between the self and the other

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    ‘Civilising’ Urban Life: Governmentality, Alcohol, and Sydney’s Public Spaces 2005-2022

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    Over the last two decades Sydney experienced seismic shifts in nightlife regulation, driven by a moral and expert panic over alcohol-related violence and local drinking ‘culture’ which culminated in the 2014 ‘lockout laws’ that significantly diminished aspects of the city’s cultural life. At the same time, public spaces within the city have been subject to intensifying processes of privatisation, commercialisation and enclosure. My thesis asserts the critical role that alcohol governance has played in attempts to ‘civilise’ urban life and re-articulate public space within the post-industrial context. Locating my research at the interstices of cultural studies and critical drug and alcohol studies, critical policy studies, urban studies, and cultural history, I draw on a range of sources including media discourses of drinking, televised dramatisations, and government strategy papers in order to explore when the recent drift toward enclosure of public space began, the discourses that undergirded it, and what the implications have been for public life in Sydney. I interrogate the linear-causal framing of state-government and media discourses on ‘alcohol-related violence’, as well as the City of Sydney’s classist pushback to this panic through the urbanist discourse of ‘civilised drinking’. I reveal the ways in which invocations of ‘drinking cultures’ tend to obscure histories of technical interventions, and highlight alcohol-free zoning as a key technology in the privatisation of public space through the mechanism of the liquor license. Recent attempts to re-activate public space in the context of the COVID-19 pandemic are haunted by memories of convivial publics, and remain characterised by a lack of attention to forms of governmentality that serve to impede publicness. I conclude that these interventions are premised on class and other forms of social segregation and that they threaten a more expanded sense of public space

    Aerial Drone-based System for Wildfire Monitoring and Suppression

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    Wildfire, also known as forest fire or bushfire, being an uncontrolled fire crossing an area of combustible vegetation, has become an inherent natural feature of the landscape in many regions of the world. From local to global scales, wildfire has caused substantial social, economic and environmental consequences. Given the hazardous nature of wildfire, developing automated and safe means to monitor and fight the wildfire is of special interest. Unmanned aerial vehicles (UAVs), equipped with appropriate sensors and fire retardants, are available to remotely monitor and fight the area undergoing wildfires, thus helping fire brigades in mitigating the influence of wildfires. This thesis is dedicated to utilizing UAVs to provide automated surveillance, tracking and fire suppression services on an active wildfire event. Considering the requirement of collecting the latest information of a region prone to wildfires, we presented a strategy to deploy the estimated minimum number of UAVs over the target space with nonuniform importance, such that they can persistently monitor the target space to provide a complete area coverage whilst keeping a desired frequency of visits to areas of interest within a predefined time period. Considering the existence of occlusions on partial segments of the sensed wildfire boundary, we processed both contour and flame surface features of wildfires with a proposed numerical algorithm to quickly estimate the occluded wildfire boundary. To provide real-time situational awareness of the propagated wildfire boundary, according to the prior knowledge of the whole wildfire boundary is available or not, we used the principle of vector field to design a model-based guidance law and a model-free guidance law. The former is derived from the radial basis function approximated wildfire boundary while the later is based on the distance between the UAV and the sensed wildfire boundary. Both vector field based guidance laws can drive the UAV to converge to and patrol along the dynamic wildfire boundary. To effectively mitigate the impacts of wildfires, we analyzed the advancement based activeness of the wildfire boundary with a signal prominence based algorithm, and designed a preferential firefighting strategy to guide the UAV to suppress fires along the highly active segments of the wildfire boundary

    Summer 2023 Full Issue

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    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    On the role and opportunities in teamwork design for advanced multi-robot search systems

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    Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges

    Safe Multi-Agent Reinforcement Learning with Quantitatively Verified Constraints

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    Multi-agent reinforcement learning is a machine learning technique that involves multiple agents attempting to solve sequential decision-making problems. This learn- ing is driven by objectives and failures modelled as positive numerical rewards and negative numerical punishments, respectively. These multi-agent systems explore shared environments in order to find the highest cumulative reward for the sequential decision-making problem. Multi-agent reinforcement learning within autonomous systems has become a prominent research area with many examples of success and potential applications. However, the safety-critical nature of many of these potential applications is currently underexplored—and under-supported. Reinforcement learn- ing, being a stochastic process, is unpredictable, meaning there are no assurances that these systems will not harm themselves, other expensive equipment, or humans. This thesis introduces Assured Multi-Agent Reinforcement Learning (AMARL) to mitigate these issues. This approach constrains the actions of learning systems during and after a learning process. Unlike previous multi-agent reinforcement learning methods, AMARL synthesises constraints through the formal verification of abstracted multi- agent Markov decision processes that model the environment’s functional and safety aspects. Learned policies guided by these constraints are guaranteed to satisfy strict functional and safety requirements and are Pareto-optimal with respect to a set of op- timisation objectives. Two AMARL extensions are also introduced in the thesis. Firstly, the thesis presents a Partial Policy Reuse method that allows the use of previously learned knowledge to reduce AMARL learning time significantly when initial models are inaccurate. Secondly, an Adaptive Constraints method is introduced to enable agents to adapt to environmental changes by constraining their learning through a procedure that follows the styling of monitoring, analysis, planning, and execution during runtime. AMARL and its extensions are evaluated within three case studies from different navigation-based domains and shown to produce policies that meet strict safety and functional requirements
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