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U.S. Energy Perceptions: 2023 SPEER Survey Findings
Get PDFUsing an online survey, we analyze important predictors of energy preference for both fossil fuels and renewable energy sources. This report provides new insights and confirms findings from earlier studies on energy preferences. Our results support previous research that highlights politics as a crucial determinant of energy preferences among Americans. Additionally, we find that religious factors and individual demographics play significant roles. Additionally, we find that religious factors and individual demographics play significant roles. Political affiliation, ideology, religious beliefs, gender, and biblical literalism strongly influence attitudes towards various energy sources. Conservatives, Republicans, women, and biblical literalists generally show less support for renewable energies and greater support for fossil fuels. These findings suggest that targeted communication strategies addressing gender specific concerns and engaging with religious communities, particularly those with literal interpretations
of scripture, could be effective in promoting the energy transition. We conclude with a discussion on the importance of this research for motivating social science scholarship on energy preference.N
Bridging the Gap between Sparse Matrix Computation and Graph Models
Get PDFSparsity manifests itself in a multitude of modern High-Performance Computing applications including graph neural networks, network analytics, and scientific computing. In sparse matrices, the majority of values are zeros. Traditional methods of storing and processing dense data are unsuitable for the new nature of sparse data, as they end up wasting storage and compute on zeros. Hence, a variety of sparse data formats that store only the non-zero elements were proposed in literature to provide a compact representation of sparse data. Performance of operations on sparse data mainly depends on the sparse data format used for storing the data, as the algorithm needs to closely match the sparse data format. However, choosing the optimal sparse data format for the input sparse matrix is non-trivial, as the optimal format depends on the sparsity pattern of the input sparse matrix. For example, in sparse matrix-vector multiplication (SpMV), for the same input sparse matrix, using different sparse data formats can yield highly variant performance. The best format being the one that closely matches how the non-zeros are arranged within the matrix. Additionally, performance prediction for operations involving sparse matrices is not as straightforward as it used to be for the dense case. For dense computations, dimensions and strides suffice for performance predictions as they provide a sense of the number of floating point operations (FLOPs) to be performed, and how this number compares to the architecture properties (peak FLOPs, number of processing elements, etc.). On the other hand, sparse matrix dimensions do not directly convey useful information about the total number of operations to be performed, since the majority of elements are zeros and do not contribute to the total number of FLOPs. Moreover, existing work on sparse operations optimizations mainly depends on a discrete set of matrices, limiting the ability to generalize observations. To address these challenges, we identify the sparsity pattern as the main driving factor for performance. First, we propose an extensible classifier framework to automatically identify the sparsity pattern of the input sparse matrix. This framework uses graph neural networks (GNNs) by representing the input sparse matrix as a graph, and then learning the structural relationship between nodes in the matrix (graph). Our framework achieves up to 98% classification accuracy on full graphs, the same accuracy for scrambled matrices, and 92% accuracy for small random subsamples taken out of original graphs. Second, we use graph models as a proxy to generate large-scale synthetic sparse matrices. We propose another modular framework to study the correlation between the graph model parameters, and the structure of the resulting graph and its tolerance to noise during the generation step. Third, we also use graph models for a performance evaluation framework that can assist in finding the best sparse format for a given graph model on a given architecture, utilizing the graph model parameters as a representative set of features to predict performance, and providing a more robust way of visualizing sparse matrix operations performance. This framework also takes into consideration noise in the matrix generation step, and evaluates the extent of noise to which performance can still be predictable based on the graph model parameters. Our results show that sparse computations need richer models to categorize sparse matrices in terms of structure, study the sensitivity of such models even for one structure, and tie performance to a more descriptive set of parameters
An analysis of the intra-district variances in PTA revenue among elementary schools
Get PDFThe purpose of this quantitative resource equity study was to use an equity audit to examine the relationship between intra-district school per-pupil instructional expenditure coupled with per-pupil Parent-Teacher Association (PTA) group revenue at twenty-five elementary school sites in an anonymous suburban school district to determine the effect on the per-pupil instructional expenditure. Many studies reviewed the effect of additional revenue on student performance. Parent-teacher Association revenue was an additional resource for individual schools that is not accounted for in school budgets. The outward appearances of prospering districts can mask intra-district inequities caused by non-profit groups affiliated with each school. Parent-teacher association groups may have a subsidiary effect on equitable activities and spending by further marginalizing students at the elementary school sites within a district.
This study used various equity measurements to assess the revenue variances between per-pupil instructional expenditure and per-pupil PTA revenue at each of the elementary schools in an anonymous suburban school district. Four categories were analyzed: per-pupil instructional expenditure for 25 elementary schools, per-pupil instructional expenditure for 15 elementary schools, per-pupil PTA revenue for 15 elementary schools, and per-pupil instructional expenditure combined with per-pupil PTA revenue for 15 elementary schools. The assumption was per-pupil instructional expenditure and per-pupil PTA revenue would vary between the elementary schools, and those differences were attributed to various predictor variables. The study found low levels of inequity in per-pupil instructional expenditure and moderate inequity in per-pupil PTA revenue. The coefficient of determination and multiple regression found the predictors of gifted/talented percentages, special education percentages, and teacher experience statistically significant in all 25 elementary schools’ instructional expenditure. Only teacher experience was statistically significant in the sub-sample of 15 elementary schools’ instructional expenditure. The predictor variables for the per-pupil PTA revenue and per-pupil instructional expenditure did not have a statistically significant relationship in the sub-sample of 15 elementary schools. The study concluded per-pupil instructional expenditure and per-pupil PTA revenue were statistically inequitable, but the predictor variables varied in statistical significance for the relationships with each of the sub-sample groups.
Keywords:
adequacy
equity
critical resource theory
instructional expenditure
PTA revenu
Apparitional representations: disability history, reparative descriptions, and ethical failings in a special research collection
Get PDFReparative description is a trend in archival scholarship that seeks to address past harms caused by archives that misrepresented and silenced historically marginalized communities in their collections. Identifying and better representing disability history in archives is a part of this trend with many archivists publishing either theoretical approaches or reparative description work that focus mostly on the end product. Few published works, whether a blog post or academic article, consider the challenges and potential failures of remediating descriptions in archives that do not have collections focused on disability history. For archives, such as the Western History Collections, disability history is a miniscule part of its collections, adding to the already difficult process of remediating descriptions. In this thesis, I outline my process for remediating descriptions using a variety of theories from archival, trauma, feminist, and disability studies in order to illustrate the professional and ethical challenges of crafting adequate descriptions that better represent the disabled subject in the Western History Collections. Using ghosts and haunting as a foundation for approaching reparative description work at a special collection that never focused on disability history, I consider the realities of bringing historically marginalized disabled persons to the forefront of archival descriptions while highlighting the importance of making the invisible work of remediation in archives visible
Extending Reality: Understanding Moderating Variables in Spatial Learning with Augmented and Virtual Reality
Get PDFDigital learning tools like smartphones, tablets, and extended reality (XR) devices are increasingly accessible to students and teachers. These devices have the potential to be powerful educational tools and can simplify complex tasks, but they are often adopted without a full understanding of their effects on learners. This dissertation aims to evaluate how spatial technologies such as augmented and virtual reality (AR and VR), collectively referred to as extended reality, impact the learning process, and how these effects might depend on the characteristics of each individual learner. The research presented here expands on existing research by utilizing different categories of devices and multiple modes of visualization, analyzing their effects within three distinct settings and a diverse pool of participants. In our first analysis, we evaluated learning outcomes for students who used XR in a university classroom to explore key concepts from marine ecology and conservation. We found that students utilizing XR had significantly higher rates of learning achievement in a pre/post-test experimental design. While gender identity and recreational gaming habits had no significant relationship with learning achievement, women enjoyed the XR activity more than men and wished to use XR in educational contexts again in the future. In a second analysis, we evaluated learning outcomes for high school students who used gamified AR while visiting a public aquarium. In this informal educational setting, we again found that AR significantly improved learning outcomes, but we also observed a significant gender effect. Boys aged 14-18 displayed greater learning achievement than girls in response to the gamified mobile AR activity. In a third analysis, we evaluated the usefulness of XR tools for navigating a novel indoor environment. We found that stereoscopic AR improved participants’ abilities to navigate a novel indoor environment, compared to a control group with no assistance, but participants using monoscopic AR performed worse than the control group. When participants repeated the navigation task two weeks later without the use of a device, all groups demonstrated significant improvements in performance. The control group showed the greatest improvements, while participants in the stereoscopic AR group retained the least distance and time traveled. We found a significant relationship between navigational performance and the age of the participant, and a significant interaction between age and gender, but no relationships between navigational performance and gender or spatial thinking ability. Overall, this dissertation provides further evidence that XR technologies can be powerful educational tools. In all three chapters, we offer strategies and suggestions for educators who wish to implement XR in their classrooms or field trips
Using Satellite Observations to Understand and Project the Urban Expansion Dynamics of the West Africa Urban System
Get PDFThe purpose of my dissertation is to improve understanding of historical, current, and future infill and sprawl expansion and their causes across primary and secondary cities. The West Africa urban system (WAUS) is a global hotspot of rapid urbanization and urban expansion. Although urbanization is associated with industrialization and economic growth, most African governments have limited resources, services, and infrastructure to manage the negative impacts (e.g., biodiversity, habitat, and cropland losses) associated with urban expansion, including sprawl that expands the urban footprint and infills within previous urban developments. Previous studies on urban expansion in West Africa have mostly focused on a few individual primary cities with over one million urban populations. The numerous secondary cities with less than one million urban population are also important components of the urban system, providing services, markets, and education centers for many rural residents in West Africa. Also, cities are increasingly interconnected, and changes and associated impacts extend beyond localized scales, requiring detailed understanding of urban expansion dynamics for the entire network of cities. This research achieved the purpose of the dissertation in three interconnected ways. First, I used satellite data to comprehensively analyze infill and sprawl expansion from 2001 - 2020 across 1603 cities in Ghana, Togo, Benin, and Nigeria. The findings from this research show that more than half (54%) of the expanded area occurred in smaller cities, and 73% was sprawl. Sprawl-to-infill ratios were higher in smaller cities than in larger cities, and the annual expansion rates of larger cities decreased over time while those in smaller cities were stable or increased. This research also found that proximity to larger cities increased smaller cities' expansion rates in Nigeria, but more remote cities had higher expansion rates in Ghana, Benin, and Togo. Second, I used a mixed-method approach to understand the causes of sprawl and infill expansion in primary and secondary cities, using Ghana as a case study. In primary cities, high cost of land and rent, travel costs, and fewer land allocation regimes were important considerations, whereas in secondary cities, lower land, rent, and travel costs and more diverse land allocation were important factors. These factors are more favorable in the periphery because land and rental costs are mostly cheaper, especially in secondary cities; this likely explains the higher sprawl-to-infill expansion. Interviews also suggest that new developments reflected the aims of the powerful urban stakeholders, including the chiefs and political elite. Third, this research used FUTure Urban-Regional and Environment Simulation (FUTURES) to model future urban expansion across Ghana, Togo, Benin, and Nigeria from 2020 – 2050. This research finds that input variables varied across small, medium, and large cities. New developments were mostly influenced by proximity to previous developments, whereas elevation and hillshade were generally less important across cities. This research also finds high annual expansion rates of 3.8% to 14.3% across the study area, with most expansion occurring between 2030 – 2040. Overall, this dissertation research provides a detailed analysis of the historical, current, and future urban expansion of cities with varying population sizes in WAUS. It finds that smaller and more numerous cities contribute substantially to urban expansion and need to be incorporated into national and regional assessments of urban growth and its impacts
Experimental Investigation and Characterization of Turbulent Spots on a Wedge in a Hypersonic Boundary Layer
Get PDFThe Boundary Layer Transition (BLT) is a serious problem for high-speed vehicles since a vehicle experiences a huge thermal load during a hypersonic flight. Especially, the thermal load becomes so significant that it may destroy the vehicle by altering and/or melting the surface structure in the BLT region. Therefore, it is essential to predict the location of BLT for designing hypersonic vehicles. BLT has been investigated for several decades. Previous experimental and theoretical investigations indicated that the merging of turbulent spots results in a fully developed turbulent boundary layer. Therefore, it is important to study the characteristics of turbulent spots for a better understanding and accurate prediction of BLT location. In this study, the boundary layer flow on a wedge model was investigated using pressure sensors, and turbulent spots were visualized by measuring the heat flux on the surface of a wedge model using a fast-response Temperature-Sensitive Paint (TSP). The frequency analysis may indicate the boundary layer flow was transitional. The global heat flux distribution derived by TSP measurements visualized turbulent spots propagating downstream. Based on the global heat flux distribution, the propagation velocity, the peak heat flux, the lateral spreading angle, the vertex angle, and the number of turbulent spots that appeared during test durations were obtained. Some of the results show good agreement with data available in the previous research, showing the ability of the TSP used in this study
The Performance of Self: The Story of Cora Youngblood Corson
No full textIn the early twentieth century, the name Cora Youngblood Corson regularly appeared in newspapers and magazines around the world. She was a fixture on the concert stage and vaudeville circuits. As a euphonium and tuba player, she was praised as being one of the greats of her time. She was a woman of high class who became independently wealthy, moved in the rarified world of celebrities, and her endorsements drove sales of musical instruments. Performing in full tribal regalia as a Native American she subverted cultural stereotypes by portraying Natives as modern entertainers rather than people of the past. Crowds flocked to theaters to see this Native woman who grew up on the plains of Oklahoma perform Indian songs, opera, and jazz with skill unmatched by any other. However, Cora had no Native heritage. Cora Youngblood Corson’s life exemplifies another form of defrauding Native Americans in Oklahoma. Her ability to continually reinvented herself by manipulating racial, national, and class representations to remain unique in vaudeville highlights a seldom discussed form of representation of self