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Improving the Fieldwork Supervision Experience with Micro-Training on Self-Advocacy and Self-Efficacy
No full textFieldwork experiences for students pursuing certification in applied behavior analysis can be challenging when a supervisor’s behavior does not align with expectations. However, students in these experiences often do not know how to advocate for what they need. This research uses a multiple-baseline single case design to examine an intervention to teach students self-advocacy skills using a micro-training and task analysis. Also examined were self-efficacy skills related to the process of supervision. Results showed an effect of the intervention for two participants and varied effect for two others. Participants found the research to be socially valid. Changes to intervention for efficiency of learning, limitations, and future research directions are discussed
IMPROVING INDIVIDUALIZED EDUCATION PROGRAMS WITH BEHAVIOR SKILLS TRAINING: BALANCING QUALITY AND EFFICIENCY
No full textThe development and implementation of Individualized Education Programs (IEPs) are crucial to a student with disabilities' access to Free and Appropriate Public Education (FAPE). However, many educators lack the necessary training and ongoing support to enhance the quality of the IEPs they develop and implement. This three-article dissertation begins with a systematic literature review to summarize the IEP professional development literature. The results of the systematic review were used to design a multiple-baseline single case study to examine an intervention package to teach special educators to write high-quality Present Levels of Academic Achievement and Functional Performance (PLAAFP) statements, which are the foundation of a student’s IEP. Results showed a functional relation between the use of the Behavior Skills Training (BST) model and an increase in PLAAFP quality. The third article outlines the procedures employed in the multiple baseline study and translates results for practitioners. Finally, implications for researchers, practitioners, and policymakers are presented
Moving From Plural to Multicultural Organization: The Case of SafeHarbor International
No full textThe integration of culturally-distinct employees remains a desirable, yet challenging, dream to realize in the modern workplace. Field research at a multinational, multilingual organization, SafeHarbor International, was conducted as a means of investigating multiculturalism in organizations. The case of SafeHarbor explored the sensemaking cues employees received regarding multicultural integration, and how these cues influenced organizational outcomes. The study addressed two primary objectives: What cues did employees receive for and against multicultural integration (RQ1) and what are the outcomes of those cues in relation to Cox’s (1991) model of integration (RQ2). Findings for RQ1 revealed conflicting cues, which created an ambivalent sensemaking environment with both “Green Lights” (indicating support for integration) and “Red Lights” (indicating resistance to it) reported. For RQ2, outcomes relating to Cox’s dimensions of integration reflected both pluralistic and multicultural organizational status. This study contributes to the literature by offering insights into the liminal organizational space that can exist between pluralism and multiculturalism within a single organization, documenting ambivalent cues, and advancing research on sensemaking in multicultural contexts. Findings also offer the original concept of silicon curtain (i.e., management’s overuse of specific technologies at the expense of relationship development), which involves the limitations of technology in facilitating integration between culturally-distinct groups within an organization
“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs
Full text linkFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end-users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi-model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post-downscaling. We recommend that subset-selection methods account for this possibility and that decision-relevant downscaled climate projections provide proper descriptions of fitness-for-purpose and essential caveats, so that non-specialists can interpret the results with an appropriate level of confidence.Ye
It Is Not Our Fault Yet: Multi-Attribute and Machine Learning Study For Improved Upper Basement Fault Detection In An Area Of Carbon Capture Utilization and Storage: Decatur, Illinois, USA
Full text linkIdentifying and interpreting faults is critical in many geological prospects, from traditional well planning and operations to energy transition ventures such as Geothermal and carbon capture, utilization, and storage (CCUS). Effective geohazard analysis can reduce the operation time, cost, and uncertainty. More importantly, with new technologies such as CCUS, where public perception and support are sensitive, it is important to decrease analysis uncertainty and prevent large-scale reactivation events.To improve the accuracy of fracture delineation, a machine learning (ML) and multi-attribute approach was employed in Decatur, Illinois— where microseismicity has been induced in the rhyolitic basement related to CCUS. Due to the poor seismic imaging resolution of the basement and the potential presence of sub-seismic faults, traditional geometric attributes (e.g., coherence and curvature) are not sufficient alone. Structure-oriented filter (SOF) is utilized before the application of the multi-attributes which resulted in increased fault confidence and connectivity. Seismic attributes candidates are gray level co-occurrence matrix entropy (GLCM), fault enhancement of energy ratio similarity (ERS), most positive curvature (k1), most negative curvature (k2), and aberrancy. For ML, a pre-trained convolutional neural network (CNN) was utilized while generative topographic mapping (GTM) was calculated using the aforementioned candidate attributes. Our findings show ERS and CNN can effectively map larger-scale fault patterns where curvature and aberrancy are better for faults seen as flexures or folds. However, only some faults detected from ML and attributes coincided with the location of microseismic events. This is most likely due to a combination of factors such as the poor quality of seismic image at the basement, high viscoelastic attenuations at deep depths, and small vertical displacement of sub-seismic faults
DEVELOPMENT OF A MACHINE-LEARNING ENHANCED HIGH PERFORMANCE METHANE SENSING INSTRUMENT FOR FIELD APPLICATIONS
Full text linkMethane - a well-known potent greenhouse gas - has significantly contributed to globalwarming through their emissions from both natural sources, like wetlands, and human activities, including particularly the fossil fuel production sectors. The earlier to detect the emission sources, would allow a fast response to deploy the mitigation strategies to contain methane from releasing the environment. Therefore, making accurate and fast monitoring of methane emission is vastly crucial, particularly in the oil and gas industries where large-scale emissions are prevalent. However, the existing challenges on the field deployable sensing solutions still exist. They are either too costly involving human participation or with unstable or low sensitivity performance to provide an accurate and low false alarm sensing outcomes. Having this motivation, this thesis presents the development of a new cost effective, high performance and environmentally robust methane sensing instrument based on the NDIR (Nondispersive Infrared) sensing method and machine learning enhancement algorithm, which could be largely distributed to form a real-time methane monitor- ing network that can be strategically positioned for scalable and comprehensive area coverage, such as from facility-level to production basin or even regional level. Specifically, the thesis includes six chapters, starting from the background introduction to provide an brief overview of the existing methane emission issues and monitoring studies. Chapter two will provide a systematically review of the point sensor tech- nologies that can be used for creating real-time sensing network in the field to detect methane emission dynamics. Chapter Three will primarily describe the design of the circuit board and the overall setup of the device. After finalizing the design of a single device, Chapter Four will focus on the selection and training of the machine learning models used to process the data and mitigate environmental influences. Chapter Five will present the three validation methods we employed to verify the accuracy of the device’s readings: in-lab validation, open-area validation, and field validation
PULSED LASER DEPOSITION AND CHARACTERIZATION OF LANTHANUM-DOPED CALCIUM STANNATE EPITAXIAL THIN FILMS FOR ULTRA-WIDE BANDGAP OXIDE SEMICONDUCTOR APPLICATIONS
Full text linkCaSnO3 exists as an ultra-wide bandgap (UWBG) semiconductor with a bandgap of 4.1eV-4.4eV, which, when engineered correctly, can be advantageously used as a material for transparent thin film transistors (TFTs) or for high-power electronic applications. CaSnO3 has been regarded as an “undopable” material, due to the challenges of incorporating dopants in its perovskite structure, and the complex mechanisms that can govern the doping behavior. While there have been improvements made regarding its thin film synthesis with molecular beam epitaxy (MBE), the literature for doped thin films of CaSnO3 deposited by pulsed laser deposition (PLD) is limited and attempts to properly dope the compound have been unsuccessful. In this study, the ability to dope and grow epitaxial thin films via PLD is challenged by undertaking an extensive investigation of powder preparation routes, sintering methodologies, and thin film growth condition optimization. While no semiconducting behavior was apparent, multiple doping levels were examined, evaluating phase purity, structure deviation from bulk references, and dopant incorporation. Epitaxial thin films were deposited under varying background oxygen pressures and substrate temperatures to optimize crystalline quality. Analysis using x-ray diffraction demonstrated promise in the potential of PLD as a viable route for fabricating doped UWBG CaSnO3
Host habitat shapes the gut microbiomes of insular reptilian hosts in the Philippines
Full text linkFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Islands have long served as ideal, replicative “natural laboratories” to help identify the mechanisms that shape the diversity and distribution of plant and animal communities, and a burgeoning body of literature has utilized island-like systems to better understand the processes that shape microbial community diversity. Despite this expanded application, few studies have explored patterns of microbial diversity spanning true islands, especially among communities of microorganisms that colonize vertebrate hosts (i.e. microbiomes). Here, we use 16S ribosomal ribonucleic acid microbial inventories to elucidate the roles that host evolutionary history, host habitat, host microhabitat, and geographic location play in the assemblage of gut microbiomes among reptilian hosts spanning multiple islands in the Philippines. Host habitat and microhabitat explained most of the variation in gut microbiome diversity observed among our focal hosts. Although we identified some significant differences in microbiome diversity across two of the host suborders (Lacertilia and Serpentes) and some host families, we did not find evidence of phylogenetic signal. We also conducted analyses of microbiome diversity across various geographic scales, and found that hosts inhabiting the same island, but different localities, did not possess significantly different gut microbiomes. However, the gut microbial diversity of hosts inhabiting distinct islands were significantly different across numerous measures of microbiome diversity. Results from this robust, comparative study contribute to our growing knowledge of the host-associated and geographic mechanisms that shape the vertebrate gut microbiome and represents one of the first studies to characterize variation in gut microbial communities among vertebrate hosts inhabiting multiple Philippine islands.Ye
ARCHITECTURAL DESIGN, BIM, AND ARTIFICIAL INTELLIGENCE: A REVIEW OF RULE-BASED AND MACHINE LEARNING APPLICATIONS
Full text linkArchitectural design projects can reach high complexity, long lists of requirements and intricate architectural programs, require effective ways to assess and predict building performance. Building Information Modeling (BIM) has proven to be a valuable tool in improving the design process by offering a range of resources for managing geometric data and building performance. However, BIM's current limitations, particularly in terms of geometry and function representation and its ability to predict performance, hinder its full potential. In this context, Artificial Intelligence (AI) is presented as an opportunity to enhance BIM workflows. AI's capabilities in prediction, automation, and analysis have the potential to improve design processes, offering more accurate simulations and predictions. This research explores how AI can be integrated into BIM-supported architectural design by conducting a case study using early-stage design AI tools, specifically Autodesk Forma. The study examines how AI-powered design tools can assist in the creation of building designs that meet specific performance criteria, such as energy efficiency, sustainability, and compliance with local regulations. Through this case study, the research aims to provide insights into how AI is helping to refine the BIM design process and to uncover the potential of AI in overcoming BIM's limitations. Ultimately, the findings will highlight the growing role of AI in architecture and offer perspectives on its future integration into design workflows
Bayesian Posterior Exploration and Predictive Analysis of Finite-Fault Earthquake Models
Full text linkAlthough earthquake rupture process is inherently complex, recent inversion techniques enable data-constrained physical models that image the spatiotemporal evolution of fault slip. Among these methods, Bayesian inversion framework yields an ensemble of plausible models to reproduce data, in contrast with conventional methods that produce a single best-fit solution. These posterior model ensembles are valuable for exploring relationships among physical parameters, generating downstream physical observables, and quantifying interlinked uncertainty structures. However, large ensembles (from thousands to millions) of high-dimensional models pose computational challenges. Here, we analyze inferred rupture properties and predictive observables of five Bayesian kinematic finite-fault models for large megathrust earthquakes in Japan, Chile, Ecuador and Nepal: 2011 Mw9.0 Tohoku, 2014 Mw8.1 Iquique, 2015 Mw8.3 Illapel, 2016 Mw7.8 Pedernales, and 2015 Mw7.8 Gorkha. We compare joint probability distributions of kinematic rupture parameters, including slip, rupture speed, rise time, and average slip rate, and assess relationships locally and fault-wise across events. We use posterior model ensembles to compute static surface deformation and on-fault stress changes, dynamic ground motion, and their spatiotemporal covariances. We find patch-level correlations are robust between some parameters given expected tradeoffs, but intra-event correlations between most parameters are variable across events. This result conflicts with predicted relations, e.g., between peak slip rate and rupture speed, established by dynamic rupture simulations, suggesting limitations in adopted physics and/or inversion approaches for heterogeneous rupture scenarios. Our static forward simulations reveal characteristic patterns in the predicted deformation and stress changes across the explored megathrust settings. The predicted surface deformation exhibits spatial variations in amplitude and direction largely inherited from source properties, whereas on-fault static stress is more heterogeneous in space. The variability in uncertainty is influenced by station coverage, source characteristics, and the data-model error structure. Despite source heterogeneity, the event-average static elastic strain drop is tightly constrained within ~100–300 microstrain, while static stress drop spans ~5–25 MPa. In the dynamic simulations, displacement waveforms stabilize earlier and faster in smaller earthquakes. Predicted peak ground displacements (PGD) exhibit near-field discrepancies with empirical scaling laws due to finite-source and directivity effects. Our findings can inform physics-based earthquake modeling and improve quantification of rupture complexity and hazard impacts