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What's the verdict: how disgust dictates jury verdict and the mitigating role of symbolic cleansing
Get PDFApproximately 1% of the US prison population (about 20,000 individuals) is currently wrongfully convicted (The Innocence Project, 2023). Wrongful convictions are influenced by ambiguous evidence, cognitive heuristics, and moral emotions, which affect legal decision-making (Baldwin & McConville, 1979). The present study assessed the effect of disgust-inducing and cleansing images on mock jury guilt ratings. One hundred fifty-two participants were randomly assigned to one of four conditions: disgust-inducing and cleansing images, disgust-inducing images only, cleansing images only, and no images. Participants read an ambiguous vignette about a crime and battery charge, then rated the defendant’s guilt on a 7-point Likert scale. The findings showed, in a marginal effect, that exposure to cleansing images following disgust-inducing images resulted in lower guilt ratings, indicating that moral cleansing mitigates heuristics formed by the vignette and disgust-inducing images. The decreased guilt ratings in the group exposed to both cleansing and disgust-inducing images demonstrate the role of symbolic cleansing in moral restoration and reaffirm the link between moral self-assessment restoration and reduced feelings of disgust (Schaefer, 2019). Future studies should explore the impact of more arousing media and different forms of symbolic cleansing to determine how moral cleansing might influence pathogen avoidance responses and lead to fairer sentencing in the criminal justice system
Wildfire Occurrence Prediction for CONUS with the UNet3+ Deep Learning Model
Get PDFWildfire represents a risk to life and property in many areas of the United States and is of growing concern to insurance companies, legislative bodies, and the public. Accurate wildfire forecasting could allow for earlier deployment of firefighting resources resulting in less property damage and less loss of life. Accurate wildfire forecasting could lower the cost of suppressing a wildfire in progress and allow for longer lead times in communicating with the public. The purpose of this research is to explore the efficacy of applying deep-learning to the task of predicting wildfire occurrence for the contiguous United States (CONUS) in the 0-to-10-day range.
To address this challenge, I employ binary classification semantic segmentation using the UNet3+ model combined with a neighborhood loss function, Fractions Skill Score (FSS). The UNet3+ model, originally introduced for use in medical imaging, combines full scale skip connections with an encoder-decoder architecture, which allows it to capture both fine-grain detail and coarse-grain semantics simultaneously. With the neighborhood loss function, FSS, I am able to quantify model success by predictions made both in and around the location of the original fire label. I utilize two datasets as inputs to my model, first, gridMET and, second, NOAA’s Global Ensemble Forecast System (GEFS), both are commonly used by fire weather forecasters. For both approaches, my fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis fire-occurrence database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS from 1992 to 2020, updated in 2022, and combines data sourced from the reporting systems of federal, state, and local organizations.
The unique contribution of this dissertation is to advance the research at the intersection of deep learning and fire occurrence prediction. To this end, I detail two proof of concept models using familiar datasets and subject matter expert informed approaches with the goal of developing a deep learning method that can outperform current operational techniques used by forecasters for the task of fire occurrence prediction.
My first approach, described in Chapters 5 and 6, sources model inputs from gridMET, a daily, CONUS-wide, high-spatial resolution dataset of surface meteorology variables including fire danger variables. From gridMET, I source observed fire danger variables, observed weather variables, and a topography variable. I compare two models, the “All Fires” model that uses all fire occurrence instances in the label images and the “Large Lightning” model that only uses instances of fire occurrence that represent large, natural-caused fires. For the experiment, the “All Fires” model produces higher max CSI values than the “Large Lightning” model when compared on general performance and on only large lightning fire performance. The “All Fires” model also produces higher probability of wildfire when compared to both the SPC Probability of Wildfire Climatology and the “Large Lightning” model for three case studies representing the largest large lightning fires from 2018, 2019, and 2020.
In my second approach, described in Chapters 7 and 8, I source model inputs from the GEFS Reforecast dataset, a daily, 5-member ensemble numerical weather prediction model, used to produce retrospective gridded meteorological forecasts for CONUS. From GEFS, I source observed and forecast weather variables. I compare two models, the “Multi Label” model, that trains using data augmentation, and the “Pixel Label” model, that trains without using data augmentation. Both models build on the success of the previous approach by using all fire occurrences in the label images. I contextualize model performance using Max CSI and reliability calculated for three neighborhoods, 40km, 80km, 120km. For the experiment, the “Multi Label” model produces reliable results when measured at 80km and the “Pixel Label” model produces reliable results when measured at 40km. The “Multi Label” model and the “Pixel Label” model produce comparable Max CSI values for all neighborhoods for all days. Both models produce higher probability of wildfire values when compared to the SPC Probability of Wildfire Climatology on three case studies: the Camp fire, the Carr fire, and the Woolsey fire
Boys' love dramas and viewer perceptions: a study on 2gether: The Series' impact on United States' female perceptions
Get PDFInternational programming has expanded beyond its initial bounds as a result of media globalization. Streaming platforms such as Netflix and YouTube have opened up new genres to a lot of the world. Partially due to the availability of Asian-produced television shows, the popularity of various genres has increased. The "Boys' Love" (BL) drama genre is highly favored in Asia, as it delves into the romantic connections between two males. While several Asian nations are currently making BL dramas, Thailand has been highly successful and has drawn viewers from around the world.Existing literature on BL dramas is limited; this is particularly the case while looking up Thai BL dramas, particularly the episodes of 2gether: The Series. Most studies focus on fandom behavior and reactions to BL dramas in nations other than Thailand, mainly China and Japan. Thailand is often considered the most successful at making popular BL dramas, which means that dramas from that region are more likely to have a greater influence on how viewers perceive shows in the genre. This study focuses on the Thai BL drama 2gether: The Series as the program of examination because of the popularity of the series and the increased possibility that viewer perceptions may be altered. The research conducted in this study will add to the expanding, if still small, body of knowledge about BL dramas. In addition, the study will shed light on how viewers—especially women from the United States—perceive Asian culture in relation to popular Asian-produced television. The study would be useful to researchers looking into the possible worldwide effects that the very popular BL drama genre may have on the BL community as well as social, political, and foreign policies. The study's findings will also provide light on how the BL community is stereotyped in BL dramas, and whether these portrayals reinforce or replicate viewer preconceptions of the BL community. The findings may help producers and content creators have a better understanding of how depictions may serve to perpetuate unfavorable perceptions about the BL lifestyle. To examine the consumption of BL dramas and its impact on United States female audiences' perceptions of the Thai BL community and whether the perceptions are reflected in Thai BL drama themes, this study will use a triangulation method--a combination of qualitative and quantitative research. To examine 2gether: The Series influences the perceptions of United States female viewers, a survey method will be used in the form of an open-ended questionnaire. The participants for this study are adult United States female viewers. A thematic analysis will be used to examine and identify the themes portrayed in the Thai BL drama 2gether: The Series. This will provide insight into the type of themes that are portrayed and the relationship between the themes and audience perceptions of the BL community
Characterization and Assessment of Long-term Porosity of Various Cement Formulations Utilizing Nuclear Magnetic Resonance (NMR) Technology
Get PDFThe global increase in hydrocarbon demand has pushed the oil and gas industry to explore new reservoirs that were previously untapped due to the technological limitations associated with high-pressure and high-temperature conditions. This demand has concurrently driven the development of advanced well-construction materials capable of withstanding extreme environments while addressing the industry's environmental challenges. The production of conventional cement significantly contributes to carbon emissions, with approximately one kilogram of CO2 released for each kilogram of cement produced, accounting for nearly 9% of global human-related emissions. This underlines the need for sustainable alternatives to reduce the carbon footprint of well cementing.Geopolymers emerge as a viable solution, offering lower carbon emissions, high compressive strength, and superior resistance to chemical degradation, thereby aligning with the industry's sustainability objectives. This thesis explores the long-term NMR porosity behavior and pore size distribution of traditional cement (class G and class H) and geopolymers in well cementing, focusing on the effects of various additives, such as fly ash, silica flour, microcellulose, and microblock, across different curing conditions. A comprehensive study was conducted using Nuclear Magnetic Resonance (NMR) technology to monitor NMR porosity evolution over extended curing periods: 146 days at room temperature and 35 days at 75°C. Results showed that at room temperature, the addition of fly ash led to substantial NMR porosity reduction demonstrating the most significant impact by enhancing the cement matrix's densification. The NMR analysis revealed a consistent shift toward smaller pores over time, indicative of ongoing hydration and pozzolanic reactions. High-temperature curing demonstrated accelerated hydration and rapid porosity stabilization within the first 24 hours for fly ash-modified and geopolymer samples, which maintained stable NMR porosity levels throughout the testing period. The findings also suggests that increased fly ash content contributes to greater thermal stability and pore structure refinement under elevated temperatures. The study also established that prolonged curing time significantly influences NMR porosity reduction for all formulations in this work. Overall, this research provides practical guidelines for optimizing cement formulations to enhance wellbore integrity across diverse environmental conditions, with an emphasis on sustainable and durable solutions for the oil and gas industry
Legacy Analysis of Standard Model Higgs boson in the H → W W ∗ → ℓ− ν ℓ′+ν decay channel from pp collisions at √s = 13 TeV with the ATLAS detector at the LHC
Get PDFThe 2012 discovery of a Standard Model-like Higgs boson at CERN’s Large Hadron Collider (LHC) marked a pivotal moment in high-energy physics. In the years that have followed, the Higgs boson’s properties and role within the Standard Model have been refined through increasingly precise measurements of this particle in a variety of final statesand kinematic regimes. This thesis contributes to this ongoing exploration by presenting a legacy analysis of the Standard Model Higgs boson production cross sections via gluon-gluon fusion, focusing on the previously unexplored 2-jet same-flavor leptonic final state, utilizing the H → W W ∗ decay mode. The analysis leverages data from proton-proton collisions at√s = 13 TeV, collected by the ATLAS detector during the LHC’s Run 2, comprising an integrated luminosity of 139 fb−1. This work implements Deep Neural Network techniques to enhance signal identification and background suppression, rather than using mT as the discriminant variable. The innovative DNN-based approach presented here aims to deepen the understanding of the Higgs sector, optimize the ggF production process analysis, and provide new insights into the interactions of the Higgs boson with the Standard Model. Employing DNN as the discriminant variable in the ggF channels significantly improved background suppression. The inclusion of the ggF 2-jet same-flavor leptonic state improved the cross-section sensitivity by 15% in the ggF inclusive study and by 25% in the ggF 2jet channel
Everyperson's Cookbook : A Little of What You Fancy
No full textCookbook produced by the Wichita Chapter of the National Organization for Women
Venus Zine : no.6
No full textThe main goal of this zine is to showcase different female artists or bands. They do this by either promoting their recent releases, or by conducting interviews with them. A majority of the zine consists of these interviews. There is also a miscellaneous selection of articles and essays. Some are guides like how to break up with a guy or how to live cheaper in NYC. There’s essays on women’s images in the media and they promote other zines and the artists who create them
Edmon Lore: Reimagining Library Orientation Through Gameful Design
No full textOrygma is an innovative, gamified initiative that transforms traditional library orientation and information literacy instruction. By blending fictional University lore with real-world research tasks, Orygma creates an immersive learning experience that playfully engages students as they learn about library spaces and services and develop crucial information literacy skills.
Lore incorporates real-life elements as part of its fiction, so playing with fictive University lore enables us to manufacture information needs that connect with current and historical events, actual people, scientific discoveries, and news headlines in the “real world.” By gamifying library orientation, we're empowering students with durable skills and inspiring them to become curious, capable researchers from their first semester on campus. Orygma transforms information literacy instruction, fostering a love for discovery that will serve students throughout their academic careers and beyond. This presentation will demonstrate how Orygma leverages storytelling, exploration, and collaborative problem-solving to enhance student engagement and learning outcomes. This idea intends to “level up” what we previously built by expanding the gamified aspects, deepening the story, and enhancing student engagement.
Participants in this session will explore how Orygma:
-Integrates physical and digital library spaces into a cohesive learning journey Utilizes a flexible, multi-modal approach to accommodate diverse ways of learning
-Incorporates real-world research tasks that contribute to an evolving narrative
-Employs gamification techniques to motivate and inspire student participation
-Assesses learning outcomes through integrated feedback mechanisms
-Integrates Generative AI to enhance play as a medium for learning by dynamically creating personalized puzzles, adapting storylines, and providing instant, contextual feedback to playersN
Oklahoma County flora: plant surveys and floristic quality assessment
Get PDFPlant surveys were conducted at Arcadia Lake and Belle Isle on the Deep Fork River (BIDFR) in Oklahoma County, Oklahoma. The plant survey data was used to perform a Floristic Quality Assessment (FQA) at both sites. Based on an area's floristic composition, FQA produces a numerical value that quantifies how much or little environmental degradation has occurred. Arcadia Lake had a Mean C of 3.8 and FQI of 66.5, and Belle Isle at the Deep Fork River had a Mean C of 3.2 and 30.9 FQI. The FQA for Arcadia Lake indicates an ecosystem that is more intact with fewer anthropogenic stressors. In contrast, the FQA for BIDFR indicates an ecosystem in peril from anthropogenic stressors. FQA is a crucial tool for communicating to a broad audience what natural areas to prioritize for conservation. This study aimed to assess the ecological integrity of these sites and provide a baseline model for future FQA use in Oklahoma
Arban Expansion Pack Bass Clef Edition: Classic Exercises for Today's Muscians
Get PDFFirst published circa 1859 Jean-Baptiste Arban’s Grande méthode complète pour cornet à pistons et de saxhorn is an integral part of the practice routines of brass players around the globe. The First Studies, which stress an even tone and attack in the middle register are among the most frequently assigned exercises in our studies. This book expands on Arban’s work by presenting these exercises in tonal patterns that we frequently encounter in music of the twentieth and twenty-first centuries