Conference Proceedings and Presentations
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PresentationThe effects of collective trauma on Iowa farmers, their communities, and sustainability outcomes(Springer, 2024-06-14)Collective trauma refers to psychological effects that are experienced by a group of people in response to shared traumatic conditions. Farmers represent a unique population that is chronically exposed to potentially traumatic events and conditions particular to the agricultural industry. Farming communities in Iowa have experienced the farm crisis of the 1980s, decades of extreme weather events, rapidly fluctuating markets, trade wars, rising input costs, farm bankruptcies and foreclosures, and high rates of farmer suicides. Exposure to such conditions can potentially have dramatic effects on the people who experience them and the communities they live in. While research exists examining the behavioral health aspects of stress in farmers, no studies have examined the lived experiences of farmers within the framework of collective trauma and its effects on decision-making. To investigate how Iowa farmers perceive their own experiences of these potential types of collective trauma, this study conducted in-depth semi-structured interviews with farmers and farmer-oriented behavioral health experts. Particular focus is placed on how collective trauma affects individual farmers, their families, and their farming communities, as well as how this type of trauma impacts farm management decisions and sustainability outcomes. Qualitative data were analyzed using a grounded theory approach to develop a theoretical framework describing how collective trauma, in the form of environmental, financial, and community threats, impacts farm management decisions and, in turn, affects environmental, economic, and social sustainability outcomes. Potential implications for how agricultural policy can potentially address the effects and systemic causes of trauma are discussed.
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PresentationAccessible and Inclusive: A New Open-Access Handbook on DEI Metadata( 2024-05-22)
DEI metadata work has several goals: enhancing diverse representation in descriptive metadata; improving discovery of diverse resources; and mitigating negative effects of inaccurate, outdated, or offensive terminology. Through this work, librarians support their institutions’ commitments to foster a welcoming environment, provide access and opportunity, and promote a sense of belonging.
With the collaboration of the Iowa State University Digital Press, and building on the important groundwork laid by many others, five librarians wrote a handbook to provide guidelines for metadata work that focuses on diversity, equity, and inclusion. The authors' vision was to produce a one-stop, introductory reference, and make it freely available through open access.
In the presentation, the authors highlight some of the contents of the handbook and go over the creation of the book, including the timeline, the open peer review process, and publication as an open-access e-book.
Through the presentation, attendees should gain a broad awareness of DEI-related issues in metadata creation and management; learn techniques for retroactively reviewing and updating existing metadata to address these issues; and develop strategies to create metadata that better meets DEI needs.
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PresentationApple's Knowledge Navigator: Why Doesn't that Conversational Agent Exist Yet?(Association for Computing Machinery, 2024-05-16)Apple's 1987 Knowledge Navigator video contains a vision of a sophisticated digital personal assistant, but the natural human-agent conversational dialog shown does not currently exist. To investigate why, the authors analyzed the video using three theoretical frameworks: the DiCoT framework, the HAT Game Analysis framework, and the Flows of Power framework. These were used to codify the human-agent interactions and classify the agent's capabilities. While some barriers to creating such agents are technological, other barriers arise from privacy, social and situational factors, trust, and the financial business case. The social roles and asymmetric interactions of the human and agent are discussed in the broader context of HAT research, along with the need for a new term for these agents that does not rely on a human social relationship metaphor. This research offers designers of conversational agents a research roadmap to build more highly capable and trusted non-human teammates.
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PresentationReal-time state estimation using recurrent neural network and topological data analysis(Society of Photographic Instrumentation Engineers (SPIE), 2024-05-09)High-rate systems are defined as physical systems that undergo large perturbations, often exceeding 100 g’s, over very short durations, often less than 100 milliseconds. Examples include blast mitigation mechanisms and advanced weaponry. The use of control feedback to empower high-rate systems requires the capability to estimate system states of interest in the realm of microseconds. However, due to the dynamics of these high-rate systems being highly nonlinear and nonstationary, it is challenging to predict their behavior using conventional state estimation methods. To address this issue, we conduct a study that explores the integration of topological data analysis (TDA) and recurrent neural network (RNN) to improve predictive capabilities for high-rate systems. Here, TDA features are used as the input to a machine learning algorithm to determine the state of a high-rate system. We conduct practical evaluations using laboratory datasets from experiments in the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR), focusing on localizing fast-changing boundary conditions on a cantilever beam. The study demonstrates the ability of the method to classify and predict a system’s fundamental frequencies. This approach helps understand the structure of the underlying high-rate dynamics, leading to improved accuracy and precision in state estimation and prediction.
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PresentationProbabilistic Evidence Assessment Tools( 2024-05-01)