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Professional Taleworlds: In-Service Teachers Use of Small Stories as Participation in Knowledge Construction and Critical Reflection of the Sociopolitical Contexts of Education
Get PDFThis dissertation study takes up a narrative approach, grounded in both critical and feminist pedagogies, to explore how in-service teachers use small stories to participate in knowledge construction and critical reflection on issues of the sociopolitical contexts of education. Further, this study sits at an intersection of teacher learning and culturally sustaining/revitalizing pedagogies, centering teachers as transformative intellectuals by examining in-service teachers’ small stories as a pathway to enacting de/colonizing pedagogies within teacher education.Small story is a narrative approach placing epistemological and analytical emphasis on the stories told in everyday conversational interactions, which are often fragmentary, unpolished, and less coherent than the “big stories” elicited via narrative interviews. The conversational data for this study were drawn from a graduate course designed to support teachers in thinking about the sociopolitical contexts of education. Twelve in-service teachers meeting in four small groups participated in weekly online, synchronous dialogue sessions as part of a course requirement. Learnings from the study suggest that teachers tell a range of small story genres for a variety of purposes. Some genres are congruent with story types described in the literature, while other genres seem to be distinct to the teaching profession. Learnings also suggest that small stories can function as learning spaces for teachers in which they engage in non-linear and communal reflection processes. The data further suggest that small stories, as pedagogical tools, offer a view of teacher learning and perspective transformation that occurs on a micro timescale and in deictic contexts
The American Scream: Gender, Capitalism, and Power in Rural Haunted House Attractions
Get PDFIn the wake of industrialization many rural American farms embraced agritainment and learned they could commodify nostalgia and romanticized ideas of rural life. They evolved into Fall festivals featuring family-friendly agricultural entertainment, called agritainment, and reinvented a “pure” rural space, where families could seek refuge, and temporarily escape urban life. Some of these farms expanded their business into the evening by adding haunted house attractions. These farms transform at dusk, and perform a hostile identity, owning the narrative of dangerous “hillbillies” who don’t take kindly to strangers, while also performing other popular horror themes, tropes, and contemporary legends. In the same ways they commodify romantic rurality, they also commodify narratives that describe a clash between affluence and rural poverty, gendered behaviors, and invite audiences to become immersed in the narratives of the haunt to perform an elaborate folk drama. This thesis explores ten rural agritainment/haunted house attractions in Maryland, Michigan, Pennsylvania, and Virginia through participant observation and auto-ethnography. I argue that the narratives performed at these rural haunts are both problematic and empowering and using the scholarship of performance, power, and narrative, I will demonstrate that this model of agritainment reflects community, contemporary culture, and their values. Whether it’s the romantic idea of rurality, or performing familiar legend and folk horror narratives, I argue that the American agritainment is a place of evolving rural identity. A place where communities reckon with systems of power, positionality, and performance in a way to maintain cultural relevancy
Logical Modeling and Analysis of Offensive and Defensive Operations within Cyber-Physical Systems
No full textThis work is embargoed by the author and will not be publicly available until May 2028.Engineering complex cyber-physical systems (CPS) involves dealing with many assumptions about their use within their operational environment. To begin to address these concerns, modern CPSs are designed to adhere to strict safety rules and regulations with the goal of proactively addressing accidental hazardous incidents. Conversely, cyber attacks on CPS systems have evolved over the past decade while defenses are still reactive at best in understanding and mitigating these risks. CPSs are comprised of sensors, actuators, hardware, and software that interact with the physical world. Attackers can utilize these components to fabricate and/or modify the current system state to achieve their objectives. To address such threats over the lifetime of the CPS, it is paramount to identify and manage operational risks early in the system design process so possible losses are not incurred, which may include business reputation, property damage, or even injury or death. To address these threats, the work within this thesis examines the relationship between the CPS’s intended design and the behaviors of potential accidental or intentional threats that may arise within the design’s context. The intent of this work is to leverage and improve on existing formal methods that should assist with identifying and addressing risk scenarios and developing related assurance cases earlier in the engineering process. To this end, this work consists of five studies that model and address threat behaviors and responses using various means under different real-world CPS architectures which serve as case studies to identify common approaches in addressing them. These case studies focus on modeling and analyzing threats within a railroad swing bridge system, an elevator system, an aluminum-can automated manufacturing system, and a CNC system, as well as a practical attack study targeting electric motor actuation. The contributions through these studies are as follows. This project began by applying attack-fault trees towards modeling the behaviors of accidental and intentional threats and their respective remediations and analyzing the strengths and weaknesses of this model. The strengths of tree-based models, such as attack-fault trees, in relating behavioral causes of consequences in a CPS were observed through this study. However, problems were identified in using attack-fault trees when modeling finer-grained logic that may cause or prevent some issue early in the design process due to a lack of probabilistic data needed for quantitative analysis as well as prescribing defensive solutions. To address these problems at a lower level, a practical attack study on electric motors was conducted that ties attacks against physical processes to the underlying control theory required to incorporate these components within the system design. In the third study, these findings were considered through systems theory to revise tree-based risk models for CPSs in a way that considers the physical process threat surface from a top-down perspective, reducing the search space for identifying causal safety-related issues and attacks and defenses, using attack-defense trees as a base. The fourth study models a CPS’s logical behaviors using UML, LTL, and AADL, while considering error handling due to faults and attacks from the vertical and horizontal behavioral perspectives of a system-of-systems architecture. The final study resulted in the development of a library for AADL, called CADA, where behaviors due to attacks and defenses can be modeled in the context of a system using an assume/guarantee model checker. These last two studies utilize tree-based risk models to describe the results. The results of these studies improve tree-based risk models for CPSs from systems and control theoretic perspectives to qualitatively and quantitatively analyze offensive and defensive behaviors within the CPS architecture.2028-05-1
With Cane In Hand: Going Deeper Into A Bioarchaeology of Innovative Disability
Get PDFImpairment and disability are understudied and under-explored dimensions in bioarchaeological reconstruction of past lives. As methodology improves and bioarchaeologists continue to embrace new branches of social theory, in tandem with greater embrasure and application of evolutionary theory, the characteristic difficulties of studying the lives of impaired and disabled people in the past are not the barriers they once were. New approaches to the problematic paucity of information available at the surface level of archaeological remains have begun to allow deeper molecular and proteomic investigation of formerly invisible processes. Because of this opening of new avenues, traditional resistance to expanding research into disability in the past is no longer a tenable stance. Furthermore, as bioarchaeology engages with extended theories of evolution in human history and deep time, it is pertinent to address the great antiquity of impairment and disability survivorship as participant of the evolutionary history of the human lineage, rather than a static byproduct or an artifact of modernity. In accordance with an inclusive view of disabled lives as integrated in the processes and systems of human evolution, it is here proposed that disability itself represents a signature feature of the evolved plasticity characteristic of human beings at the embodied, communal, and cultural levels
The Man-Faced Bull of Gela
Get PDFThe man-faced bull appears as a numismatic type in Magna Graecia and Sicily beginning in the late sixth century BCE. The man-faced bull coinage of various cities in Sicily served as a symbol of the political entity and cultural identity of a mixed demographic. This thesis examines man-faced bull coin-types issued in Gela between 491 and 400 BCE, delving into the identity and significance of the MFB motif. Through an examination of the intricate craftsmanship, materiality, and cultural interactions of this period, this thesis sheds light on the multifaceted identity of the MFB, specifically focusing on its role in Gela
Social Salience Attribution in Opioid Use Disorder
Get PDFOpioid Use Disorder (OUD) remains a significant health burden across various societal roles. Mothers experiencing OUD demonstrate a disruption in the maternal-child bond, preventing them from adequately caring for their children. In 2016, parental drug use accounted for removing 92,107 children from their homes, and the percentage of children entering foster care resulting from parent opioid use rose from 26% to 34% between 2009-2016. Little research exists on the psychological and neurobiological processes underlying the disruption of fulfilling parental responsibilities. Neurobiological hallmarks of OUD include limbic system neuroadaptations, dysregulation, and changes in the salience network. This leads to drug craving and drug-seeking behavior at the expense of attachment, social cognition, and meta-cognition, with stronger emotional affect. Negative behavioral patterns arise as salience attribution shifts away from natural rewards like social relationships, including the maternal-child bond, toward increased drug seeking and use. In this study, a questionnaire battery of social cognition, mood symptoms, anxiety,
attachment, social networks, and metacognition was employed in a group of mothers with OUD (OUD=15), who were in residential treatment for substance dependence and a group of healthy control mothers (NHC=17). Both groups completed the questionnaire battery and underwent functional magnetic resonance imaging (fMRI) while performing the Incentive Cue task (ICT), which consisted of several cue conditions, including photos of opioids and photos of their child. This thesis focused on the response measures to the questionnaire battery and the behavioral responses (response time, RT; and error rate, ER) to the ICT. The results demonstrated group differences in the questionnaire battery related to meta-cognition, social anxiety, and social network size. The experimental group (EG) results indicated a stronger negative metacognitive belief regarding danger and uncontrollability of their thoughts, an attachment style that trends away from anxious and towards avoidant-dismissive, and a significantly smaller social network size. Further, the results showed significant group differences in behavioral measures (RT, ER) in response to the opioid cues, indicating that the EG took less time and had a higher rate of error in completing the ICT. However, there was no difference in the behavioral response in RT and ER to the own child cues. These findings highlight changes in meta-cognition, social anxiety, and social network size associated with OUD. Findings also emphasize the benefit in using cues in varying valence levels with incentive tasks as a valid experimental paradigm to study salience attribution in OUD mothers
Employing UAF Inter-Domain Traceability for Performance and Effectiveness Evaluation
Get PDF©2023 IEEE | DOI: 10.1109/SysCon53073.2023.10131056We propose a step-by-step Model-Based Systems Engineering (MBSE) process for the creation and simulation of an executable Unified Architecture Framework (UAF) model for evaluation purposes. The roll-up of Technical Performance Measures (TPMs) to Measures of Effectiveness (MOEs) is necessary for such a process, and has not been documented for the UAF. This paper is the first attempt to address this gap by demonstrating how interdependencies between these technical measures can be traced across the domains of a UAF architecture according to the ISO/IEC/IEEE 15288:2015 standard, the guidelines from the INCOSE Systems Engineering Handbook, and the UAF Enterprise Architecture Guide. The proposed process employs traceability and parametric diagrams within the UAF to produce an executable model that aids in evaluating the effectiveness of a system’s architecture. Additionally, we describe how to build a simulation within the UAF to assess a parametric diagram containing random values of TPMs. The process identifies UAF views, their constituent model elements, and the relationships that are required to build this model. We also present an illustrative example of a forest firefighting system to demonstrate the implementation and effectiveness of the proposed process. This paper is intended as a resource for systems engineering practitioners
An Agent Based Distributed Control for Networked SIR Epidemics
Get PDFThis paper revisits a longstanding problem of interest concerning the distributed control of an epidemic process on human contact networks. Due to the stochastic nature and combinatorial complexity of the problem, Finding optimal policies are intractable even for small networks. Even if a solution could be found efficiently enough, a potentially larger problem is such policies are notoriously brittle when confronted with small disturbances or uncooperative agents in the network. Unlike the vast majority of related works in this area, we circumvent the goal of directly solving the intractable and instead seek simple control strategies to address this problem. More specifically, based on the locally available information to a particular person, how should that person make use of this information to better protect their self? How can that person socialize as much as possible while ensuring some desired level of safety? More formally, the solution to this problem requires a rigorous understanding of the trade-off between socializing with potentially infected individuals and the increased risk of infection. We set this up as a finite time optimal control problem using a well known exact Markov chain compartmental Susceptible-Infected-Removed (SIR) model. Unfortunately, the problem set up is intractable and requires a relaxation. Leveraging results from the literature, we employ a commonly used mean-field approximation (MFA) technique to relax the problem. However, the main contribution distinguishing our work from the myriad works which study networked MFA models is that we verify the effectiveness of our solutions on the original stochastic problem, rather than the relaxed problem. We find that the optimal solution of the problem to be a form of threshold on the chance of infection of the neighbors of that person. Simulations illustrate our results.This thesis has been embargoed for 2 years. It will not be available until November 2024 at the earliest
Modeling and Predictability of Dust Storms and Atmospheric Dustiness over the Western United States
No full textWindblown dust and dust storms impact the Earth’s radiative energy balance and adversely affect public health. This dissertation addresses five major topics on windblown dust: model improvement (including source attribution of dust); predictability of the physics-based dust storm model; and physics-informed statistical modeling and predictions for, and climatology and variability of the observed, seasonal mean dustiness over the western United States. Accurate dust modeling is essential for understanding and predicting changes in the Earth’s climate system, as well as for guiding early warning systems and mitigation plans to reduce the adverse effects of dust. We have developed a high-resolution (1 km) dust modeling system by building upon an existing modeling framework consisting of the WRF (Weather Research and Forecasting) model, a dust emission model (FENGSHA), and the CMAQ (Community Multiscale Air Quality) model. The dust emission model utilizes new high- resolution data on land use, soil texture, and vegetation index, and new representations, such as for dust source mask, sandblasting efficiency, and roughness correction to threshold friction velocity. All those changes lead to drastic improvements in the model performance, as demonstrated by comparing a simulation of a severe, frontal dust storm in Arizona with observations from ground stations, meteorological radar, and satellite retrievals. The results show promise for developing the model into an operational dust storm-early warning system. Croplands (as opposed to desert) are traditionally ignored or improperly represented in models. Analysis of the frontal dust storm simulation indicates that croplands contributed over 50% of PM10 (the concentration of particles with a diameter less than 10 μm) in the Phoenix area, exceeding US EPA-established ambient air quality standards. The results also suggest cropland dust being the most likely cause of a dust-related traffic accident associated with minor injuries. These results imply the importance of including cropland dust sources in emission inventories and air quality simulations. Results from model sensitivity experiments have strong implications for representing the dust aerosol in air quality and climate models. Specifically, our sensitivity analysis strongly suggests using a dynamic, rather than a static, dust source mask; a physics-based, rather than a clay-based, expression for sandblasting efficiency; and up-to-date, rather than old, data for land use in a dust emission model. The analysis further suggests that meteorological nudging may be advantageous for hindcasts and warns the research community to be careful about choosing the large-scale meteorological fields to drive the WRF model; in the studied case, NARR-driven WRF-runs produced better dust simulations than NAM-driven ones that led to severe underpredictions. We used the physics-based model to conduct perhaps the first study of short-range (1–8 days) predictability of a dust storm. Here, we simulated uncertainty in meteorological initial conditions with a time-lagged ensemble method. Wind speed V is generally the most impor- tant meteorological variable for dust emission. The spread of V among ensemble members was only moderately sensitive to initial condition because of the influence of the domain’s prescribed lateral boundary conditions. The corresponding dust simulations, however, had a much larger spread, and differed increasingly from observations with lead time. As expected, ensemble spread for V increased with domain size. We used dust optical depth data based on the MODIS Deep Blue aerosol product to analyze seasonal mean dustiness (occurrence frequency of high optical depth values) over the western United States for 2003–2020 (longer than previous studies). Dustiness had a con- sistent upward trend for a southwestern region in fall and summer. Ground bareness and precipitation explained most of the observed trends in dustiness. Multiple linear regression shows that seasonal mean dustiness depends on precipitation, bareness, relative humidity, planetary boundary layer height, soil wetness, temperature, and wind speed, but the contributions of these factors depend on the region and the season. The roles of V and V3 in seasonal predictions were generally insignificant. The regression model explains ∼ 40–80% of the observed variability in dustiness, and the corresponding seasonal predictions made using the dust-controlling environmental variables from two different climate models (GFDL-SPEAR and NASA-GEOS-S2S) indicate a promising potential for seasonal predictions a few seasons in advance. Finally, a comparison between seasonal mean dustiness based on the MODIS data and MERRA-2 data showed a general mismatch between the two data sets, indicating a need to evaluate the MERRA-2 data in more detail. The findings from this dissertation research should prove useful for both dynamical and statistical modeling for windblown dust and dust storms, in addition to alerting the com- munity to the relative strengths or weaknesses of various data sets. Moreover, the study informs on the climatology and trends of observed dustiness. Finally, the results from the source attribution case study should provide an impetus to better represent in models the various dust sources with their dynamic (varying in space and time) nature represented well
Resource-Aware Control: Event-triggered Coordination with Designable MIETs and ADRC with Low Gains
Get PDFControllers are often designed assuming that certain resources are unlimited, such as sampling rates or observer speeds, but this can lead to poor performance in some cases. Control which is resource-aware accounts for practical limitations, such as event-triggered control which treats control updates as a limited resource. We consider two problems for resource-aware control. Firstly, we investigate the well-studied event-triggered consensus problem, where guaranteeing a positive minimum inter-event time (MIET) is essential because agents cannot communicate arbitrarily quickly. Existing solutions do not guarantee a positive MIET, without giving up asymptotic convergence, or requiring synchronization between agents or non-local information. We guarantee asymptotic convergence to average consensus with a positive MIET for each agent using a fully distributed control scheme. Additionally, we show a specific application of these methods to the clock synchronization problem. Secondly, we examine active disturbance rejection control (ADRC), which is a form of model-free control with an observer. Because ADRC often has too many parameters to tune by hand, many works use the simpler bandwidth parameterization. However, this may require the observer to be arbitrarily fast, which becomes impractical when the sampling rate is limited or the sensors are noisy. Instead, we show that, for a class of three-dimensional linear plants subject to disturbances, the closed-loop eigenvalues can be placed arbitrarily by proper choice of the ADRC gains, without requiring the observer gains to be arbitrarily high