20566 research outputs found
Sort by
Curated: The Impact of Architectural Sequence on the Journey through the Art Museum
Full text linkThe museum is a culturally significant institution used for learning, thought, and contemplation from its conceptualization in The Enlightenment Movement of the 17th century. The art museum has the potential to create a powerful relationship between the art and the architecture that houses it. The architecture of an art museum becomes a critical participant in the experience of art and affects the spaces in a dramatic way. If the art or architecture overpowers the other, the user experience is impacted; therefore, the connection created between the two within the museum is one of great importance. When a symbiotic connection, or a mutually beneficial relationship between the art and architecture is created harmony results, fostering a transforming experience for the museum patron. This research poses the question: What qualities create a symbiotic relationship between architecture and the viewing of art in an art museum? This research used three case studies, The Clyfford Still Museum, The Glenstone, and The Nasher Sculpture Center, and focused on extracting what architectural qualities aided in creating a symbiotic relationship between the art and architecture that resulted in a fully harmonious museum experience. A series of diagrams, photographs, and experience pastel drawings for each case study museum illuminated what elements functioned best within each museum to create harmony, or a balanced experience, with a final compilation of possible strategies for museum designers to reference to create a more accessible future for the museum typology
Stories from El Dorado: An Exploration of Art, Design, and Community Belonging
Full text linkThis project examines the history and cultural impact of the downtown arts district in El Dorado, Arkansas through a series of interviews with local community members conducted in February 2025. El Dorado is a city in Southwest Arkansas popularly known as “Arkansas’ first boomtown.” Since the discovery of oil in the area in 1921, El Dorado saw a rapid rise in population from 3,000 residents to nearly 30,000 residents. This rise in population brought in a desire for more entertainment, which led to the development of cultural institutions including the “new” Rialto Theater built in 1929 on top of the “old” theater that originally hosted 400 seats. The theater quickly became a cultural destination in the city, bringing large crowds and requiring an updated building with 1,400 seats, state-of-the-art equipment, mechanical systems, and acoustic systems.
Throughout the 20th century, El Dorado experienced a steady decline in population and started making efforts in 2017 to reinvigorate its cultural district through the formation of the Murphy Arts District, which includes an outdoor performance amphitheater, renovation planned for the Rialto Theater and an old furniture building, and the First Financial Music Hall.
Through video interviews with eight local community members, this project explores two primary questions: “How does art and design influence a community’s sense of belonging?” and “Where is the intersection between art, the built environment, and community storytelling?” More specifically, it seeks to understand how art and design have impacted El Dorado and its residents. The interviews are compiled into a 15-minute documentary that highlights stories of community and change in the El Dorado Arts District and its downtown area. Through these interviews, it became apparent that art and design is more integrated into the culture of El Dorado than the research originally showed, and these art and design aspects or programs create space for multiple communities to coexist
Creating a Website to Introduce and Inform Local Apparel Businesses’ Sustainability to Fayetteville College Students
Full text linkFast fashion has increased clothing consumption and waste, contributing to severe environmental consequences. Large retailers rapidly produce garments to keep up with microtrends, leading to consumer waste and unsustainable habits. This thesis aims to educate University of Arkansas students about fast fashion’s environmental impact and promote sustainable shopping practices. To achieve this goal a website will be created for educational purposes and will provide information on sustainability and fast fashion as well as local sustainable apparel businesses in Northwest Arkansas. The research draws from consumer behavior studies, business sustainability models, and the effectiveness of educational interventions on shopping habits. The website will offer accessible, engaging content to educate and encourage students to make eco-conscious decisions. The project\u27s success will be determined by the accessibility of the website. Ultimately, the project aims to bring awareness about fashion sustainability to the local community and inspire long-term changes in consumer behavior
Towards Multimodal Scene Graph Generation Approaches to Video Understanding
Full text linkThis thesis advances video understanding by enhancing Video Scene Graph Generation (VidSGG) through improved temporal modeling, the integration of long-range temporal dependencies via continuous updates to interaction histories, and the utilization of Large Language Models (LLMs) for scene graph reasoning. To this end, three novel datasets and corresponding approaches are introduced. First, the ASPIRe dataset incorporates interactivity annotations and leverages the Hierarchical Interlacement Graph (HIG) for hierarchical temporal modeling, providing deep insights into scene changes and effectively capturing intricate interactions. Next, the AeroEye dataset, focusing on drone videos, is paired with the Cyclic Graph Transformer (CYCLO), which establishes circular connectivity among video frames to model direct and long-range temporal relationships. Finally, the VSGR dataset, a new large-scale benchmark for advancing scene graph reasoning tasks, is introduced. Notably, the Multimodal LLMs on a Scene HyperGraph (HyperGLM) approach integrates hypergraph-based representations with LLMs, enabling more nuanced multimodal reasoning. These contributions significantly enhance dataset diversity, strengthen relationship modeling, and improve causal reasoning in VidSGG, resulting in state-of-the-art performance
Experimental Study of Novel BCSA SCC Repair Materials and Implications on Corrosion in BCSA Concrete Structures
No full textSome current building codes for structural concrete allow the use of alternative cement concrete if their structural, durability, and fire performance can be shown to be comparable to portland cement (PC) concrete. One such alternative cement, belitic calcium sulfoaluminate (BCSA) cement, is a promising standalone replacement for portland cement given its lower global warming potential (GWP), fast setting time, and high early age compressive strength. These properties also make it an ideal repair material. But repair materials often need to be highly flowable and easy to consolidate and their performance when used to repair PC concrete structures must be understood. Additional questions remain regarding its durability performance, specifically surrounding its ability to protect reinforcing steel from corrosion. This study developed novel self-consolidating concretes (SCCs) with BCSA cement and characterized their fresh and hardened properties. Cement replacements with fly ash maintained the paste content of SCC mixtures while reducing the cement content. Increased fresh performance with increasing replacement rates were observed but were weighed against decreases in hardened properties. The structural performance of beams repaired with the developed BCSA cement SCC were evaluated in terms of load-deflection behavior, ultimate load, and return of capacity. Beams repaired in the flexural tension zone had capacities within 15% of the all-PC control beams at four hours and within 4% at seven days. Beams with end zone repairs had capacities within 6% and 3% of the PC control beams at four hours and seven days, respectively. To apply repair materials such as the BCSA SCC detailed in this study, the ability of such concrete to provide long term protection from corrosion to steel rebar must also be considered. The corrosion protection provided by BCSA cement concrete was evaluated using a test method developed to compare the effect of admixtures on PC concrete’s corrosion performance. The study’s goals were to examine the role of curing time in the corrosion results for BCSA cement and to measure the effectiveness of different methods to improve corrosion performance such as decreasing the water to cement ratio, increasing cover, and the use of surface sealers. The study found that durability testing of alternative cement concrete should be done considering the maturity of the concrete, rather than arbitrarily selecting a testing age based on PC based standards. Additionally, increasing clear cover depth, lowering w/cm, and using surface coatings such as silane, were effective in increasing corrosion protection
Exploring Latent Mediation through Bayesian Regularization Methods of LASSO, Ridge, Horseshoe, Spike-and-Slab
Full text linkRegularization is a powerful tool to combat overfitting and drive sparsity in complex models. Regularization was initially applied in regression modeling but has been increasingly utilized in structural equation modeling where its utility in identifying the essential components has helped improve modeling. As structural equation models have increased in complexity both in the number of indicators but also the number of latent factors, researchers have begun to investigate how applying Bayesian regularization to these systems can further push the limits on modeling complex models with limited sample sizes. One area where research is limited is the application of Bayesian regularizations methods in models with multiple latent mediators. This study first seeks to investigate how the Bayesian regularization methods of ridge, LASSO, horseshoe, spike-and-slab, and spike-and-slab LASSO perform in capturing mediating variables of various strengths when examined under various sample sizes, number of possible mediators, and strength of latent factors. Secondly, an investigation into the sensitivity to prior settings was conducted on versions of Bayesian LASSO, Bayesian adaptive LASSO, horseshoe, and spike-and-slab. Finally, this study evaluated an empirical study investigating how these Bayesian regularization methods function with real data with multiple latent mediators
Comparison of Airborne Lidar-derived Elevation Data in Fayetteville, Arkansas, USA
Full text linkLight detection and ranging (lidar) laser scanners are prominent remote sensing tools to produce high resolution three-dimensional (3D) imagery of the Earth’s surface. These laser scanners combined with global navigation satellite systems (GNSS) and real-time kinematic (RTK) reference stations can generate some of the most accurate ground surface imagery and elevation data for terrain mapping and related applications. Lidar aerial survey is an important tool in industries such as architecture, civil engineering, forestry, geology, geography, and agriculture where digital terrain models (DTMs) can be used to examine the geographical landscape and urban industry. Currently, there are three different common laser scanning systems: traditional airborne lidar, terrestrial laser scanners (TLS), and small unmanned aircraft systems (sUAS)-based lidar. As the integration of sUAS and lidar is in a phase of rapid development, there are ongoing questions regarding the comparative precision and reliability of sUAS versus traditional airborne platforms. In this study, aerial lidar datasets collected on two distinct platforms were compared for benefits to quality and sensitivity to how the data is collected and post-processed. The lidar datasets collected were analyzed and processed using Esri’s ArcGIS Pro software and LAStools produced by Rapidlasso GmbH to generate two DTMs representing ground surface terrain. The DTMs were evaluated based on geomorphological identification, quality assessment, and lidar intensity returns. The results of the DTMs’ Pearson correlation coefficient and relative accuracy indicate strong similarities. However, the dataset collected using traditional airborne lidar was found to be a better representation of the terrain, with vegetation more identifiable in the DTM derived from sUAS lidar. The traditional airborne lidar also benefited from being captured during the “leaf-off” season and was previously post-processed with ground control points (GCPs), resulting in a more accurate point cloud of the bare earth. Future research of sUAS lidar may indicate that a more accurate DTM is possible with the application of GCPs, more analysis of intensity measurements to classify vegetation, manual classification, and evaluation of industry use. As it stands, sUAS derived lidar is more cost-effective than traditional airborne lidar and is linked to more dynamic and versatile remote sensing technologies
Geospatial Modeling of Winter-Related Property Damage in Arkansas
Full text linkStorm Data, a monthly publication by the National Weather Service with records of weather events, reports on property damage caused by natural hazards. Since 1996, the database has included winter-related events. While the property damage data is considered incomplete, no other unique, public datasets report winter-related property damage at the county level. From 1996 to 2024, Arkansas ranked #1 in winter-related property damage. To understand the factors that are driving property damage and potentially reduce Arkansas’ vulnerability to it, three groups separated by temporal periods, i.e., “2000” (01/1996 - 06/2005), “2010” (07/2005 - 06/2015), and “2020” (07/2015 - 06/2024) are examined with geospatial modeling tools. The dependent variable is calculated as the total winter-related property damage during the period divided by the decade’s population, referred to as “damage per capita”. Sixteen potentially influential variables, including storm characteristics, demographic information, environmental data, and climatological data, are identified. Then, using the most influential variables for each group, the performance of ordinary least squares, spatial-lag, spatial-error, geographically weighted regression, and multi-scale geographically weighted regression models are compared. Lastly, using the most appropriate model, the variables’ influence on damage per capita is quantified by analyzing model coefficients for each group. Results show that different factors contributed to winter-related property damage per capita over space and time in Arkansas. Accounting bias, calculated as the percentage of reports with non-zero property damages, positively influenced all three models, serving as the most influential factor for the 2020 model. The 2010 model was most significantly influenced positively by the number of hours and the presence of deciduous forests and negatively by the median income, mean precipitation, and mean snowfall. The 2000 model was the best performing group, but its coefficients were only significant over small portions of the state. In southwest Arkansas, coefficients were negative and positive for the number of heavy snow reports and mean snowfall, respectively. Coefficients were negative for the number of hours in west-central Arkansas and positive for accounting bias along the western third of the state. These results are used to make recommendations to policymakers and emergency managers
CRISPR-Cas9 Integration of Salmon Elovl2 in Zebrafish Enhances LC-PUFA Biosynthesis and Growth Across Diets
Full text linkFish are a rich source of EPA and DHA, which are well-known long-chain polyunsaturated fatty acids (LC-PUFAs). The sustainable production of these essential fatty acids poses significant challenges, mainly due to our dependence on marine fish and certain nuts. CRISPR-Cas system was utilized to insert the elongation of long-chain fatty acids-like2 (Elovl2) gene from Salmo salar into Danio rerio. Elovl2 was introduced at 10-15-minute post-fertilization, and successful integration confirmed by PCR genotyping, sequencing and expression of EGFP. Approximately 67% of injected embryos successfully exhibited EGFP and sequence results verified the gene integration of Salmo salar Elovl2 at the first start codon. Although Injected embryos exhibited lower hatching rates and higher mortality compared to the wild-type groups, but they successfully expressed the inserted Elovl2 gene, showing approximately a 7-fold increase at the early stages.
GC-MS and RT-qPCR analysis of fatty acid profiles showed that fatty acid biosynthesis-related gene (Elovl2, Fads2, Cpt1a) and adipogenesis-related genes (Fabp4, Pparg, Cebpa) were significantly upregulated in transgenic groups, with significant increases in omega-3 fatty acid levels across various body regions, including the head, trunk and tail at 30-, 60-, and 90-day post fertilization after fed different diets (brine shrimp or commercial diets). Moreover, length (mm), weight (mg) and depth (mm) were increased significantly in transgenic groups compared to the control groups, with further confirming of the RT-qPCR analysis showing the significant upregulation of growth-related genes (Ghr-a, Ghr-b, and Igf1a) and myogenesis-related genes (Myf5, Myf6, Myog, and Myod1) in the trunk and tail regions. Histological analysis also verified the increases of muscle fiber diameter in transgenic groups fed different diets, establishing a strong correlation between gene expressions and muscle hypertrophy. These findings suggest direct evidence of combining genome editing with ideal feeding plans that can significantly enhance aquaculture performance.
Overall, our findings demonstrate that incorporating the Salmo salar Elovl2 gene into zebrafish (freshwater species) enhances omega-3 fatty acid production and improves growth performance. It could be a viable approach to enhance the aquaculture productivity of species with low omega-3 content, providing a sustainable solution for improving fish quality and meeting the growing demand for nutritious aquaculture products
Molecular Mechanisms Underlying Social Learning in the Butterfly \u3ci\u3eBicyclus anynana\u3c/i\u3e
No full textLearning plays a critical role in shaping behavior, such as influencing survival and reproduction. One particularly important form of learning is mate preference learning, which allows individuals to alter their mate preference based on prior social experience. Among the different types of mate preference learning, imprinting, where early exposure to specific mating cues influences later mate choice, has been widely studied. This process is important in the context of reproductive isolation and speciation, as learned preferences can reinforce or alter eventual mate choice, potentially leading to genetic divergence between populations. Despite its importance, the molecular mechanisms underlying mate preference learning remain largely unexplored. In this dissertation, I explore the genetic and neurogenomic basis of mate preference learning using Bicyclus anynana, a butterfly species that exhibits the ability to learn mate preferences based on early social exposure. I address four key questions: (1) How does the duration of social exposure influence mate preference learning and associated gene expression? (2) What are the molecular pathways underlying positive and negative valence attribution in learned mate preferences? (3) Does the pigmentation gene yellow, known for its pleiotropic role in coloration and courtship, also modulate mate preference learning? (4) How do temporal patterns of gene expression in antennae influence female antennal receptivity? To address these questions, I used behavioral assays, transcriptomic analyses, and genome editing. This integrative approach allows me to identify candidate genes and molecular pathways involved in mate preference learning. By elucidating the genetic architecture of mate preference learning, this research advances our understanding of how learned mate preferences can contribute to reproductive isolation and speciation at a molecular level