97557 research outputs found
Sort by
Inside Rowe Hall, 2024 Scenes 30
No full textRowe Hall housed the Military Science department at Jacksonville State University. Shown is the Rowe Hall rifle training room in 2024.https://digitalcommons.jsu.edu/rotc_photos/11505/thumbnail.jp
Inside Rowe Hall, 2024 Scenes 29
No full textRowe Hall housed the Military Science department at Jacksonville State University. Shown is the Rowe Hall rifle training room in 2024.https://digitalcommons.jsu.edu/rotc_photos/11504/thumbnail.jp
JSU ROTC, 2024 Twins 2
No full textFour sets of twin took part in the Jacksonville State University ROTC program in 2024. Identified are Toni Wright, Tion Wright, Bayleigh Colston, Melenford, and Ariya DeVine.https://digitalcommons.jsu.edu/rotc_photos/11508/thumbnail.jp
JSU ROTC, 2024 DeVine Twins 1
No full textFour sets of twin took part in the Jacksonville State University ROTC program in 2024. Shown are the DeVine twins. Identified is Ariya DeVine.https://digitalcommons.jsu.edu/rotc_photos/11513/thumbnail.jp
Mystic Krewe of Apollo 47th Anniversary Ball (2024) | Costume Sketch 001
No full textMystic Krewe of Apollo held its 47th Anniversary Ball in 2024. Shown is a costume sketch for Queen Apollo XLVII. Michael Bushin wore this costume at the event. This item is contained within the Clements drama production materials.https://digitalcommons.jsu.edu/clements_costumes/1483/thumbnail.jp
Hydrologic Impact Index for the Pinhoti Hiking Trail
Get PDFAuthor note: The author of this thesis is also published under the name Allie Bobo.
This study aimed to identify flood-prone areas along the Pinhoti Trail and Chinnabee Silent Trail in the Talladega National Forest. Using the Hydrology Flood Index layer that was created using several essential data layers, the research aimed to provide campers, hikers, nature enthusiasts, and trail maintenance teams with information about areas at a higher risk of flash flooding. The Hydrology Flood Index layer rates the risk of flooding on a scale of 1 to 4, with level 1 indicating a low risk of flooding and level 4 indicating an extremely high risk. The data layers for analyzing flood hazards for the Hydrology Flood Index Map include the Soil Survey Geographic Database (SSURGO), National Land Cover Dataset (NLCD), Slope, and Flow Accumulation. The study area includes three Pinhoti campsites and the entire length of the Chinnabee Silent Trail, where high-resolution images were taken after a flood occurred in 2014
Examination of MY06 Missense Variants of Uncertain Significance Associated with Autosomal Recessive Non-Syndromic Hearing Loss Utilizing C. elegans
No full textGenetic research in human health has revolutionized healthcare. Hearing loss affects 5% globally, and additional research is needed to understand these complex conditions and variants of uncertain significance (VUS). This includes autosomal recessive non-syndromic hearing loss (ARNSHL), linked to MYO6. This study explores the MYO6 nematode ortholog, spe-15, utilizing C. elegans as a model organism. The hypothesis posits that introducing the MYO6 human variant (c.178G\u3eC (p.Glu60Gln)) into spe-15 (c.199G\u3eC (p.Gly67Gln)) results in increased unfertilized oocytes for the homozygous VUS mutant nematodes compared to the wildtype N2 nematodes. Bioinformatics confirmed variant conservation, and hands-on experiments resulted in successful nematode DNA extraction and PCR and gel electrophoresis to amplify the spe-15 VUS region. The study advances understanding of MYO6 missense variants associated with ARNSHL, supporting further CRISPR-Cas9 experiments to generate the mutant strain and examine the MYO6 VUS in vivo. Employing C. elegans offers a promising avenue for investigating the potential impact of MYO6 VUS on ARNSHL and human health.https://digitalcommons.jsu.edu/ce_jsustudentsymp_2024/1065/thumbnail.jp
Remote Sensing of Forest Structure Characteristics to Inform Prescriptive Vegetation Maintenance on the Pinhoti National Recreation Trail
No full textThis project details a remote sensing workflow intended to assist vegetation maintenance planning for the Pinhoti National Recreation Trail (PNRT). Trail maintenance is necessary to provide the best possible experience to users and to maintain the national recreation trail designation held by the PNRT. Often, this maintenance involves mechanical treatments of understory vegetation that has encroached upon the trail during the growing season. Generally, volunteers or students are sent into the field to monitor and document trail overgrowth conditions. With this information, land managers formulate a plan to deal with any issues that were recorded. The workflow documented here generates a shapefile of areas that are likely to experience trail overgrowth conditions. This data can be used to narrow the focus of initial monitoring efforts, making the planning process less resource demanding for the management agency.
The resulting shapefile is a selected range of values from a canopy height model (CHM). For the purposes of this project, canopy height and density are used as a proxy for the existence of trail overgrowth conditions. The relationship between canopy coverage and resource availability for understory plants makes canopy density a good predictor for understory conditions. The CHM is created from airborne LiDAR data and a range of its values is selected out into a new shapefile. Existing data from the PNRT brush management survey is used to determine the target canopy ranges through point sampling.
Field data collection for this project was conducted with ArcGIS Field Maps. Point and line data were taken to detail trail overgrowth. LiDAR data was sourced from the USGS at a resolution of one meter. All data used thus far has been focused on section 5 of the PNRT, near Gunthertown, AL. Data processing, analysis, and map creation were all done in ArcGIS Pro.
This project has been a collaborative effort between JSU’s Department of Chemistry and Geosciences, JSU’s Trail Science Institute, and the Alabama Trails Foundation.https://digitalcommons.jsu.edu/ce_jsustudentsymp_2024/1063/thumbnail.jp
Building a Restaurant Ordering System: From Design to Deployment
No full textThis group project, undertaken for the CS488 Database Systems course, focused on developing a functional relational database tailored for a restaurant ordering system. Through in-person meetings, social media, and collaborative platforms, we iteratively designed and implemented the database. Our process began with crafting a conceptual diagram, which was then refined with each iteration, culminating in a robust data model. Using Microsoft SQL Server 2019, this model was converted into an operational database, populated with sample data, followed by the creation of a user interface. Subsequently, we developed the front-end and integrated it with the backend. A thorough product demonstration will be presented to showcase the completed system.https://digitalcommons.jsu.edu/ce_jsustudentsymp_2024/1059/thumbnail.jp
Got Any Plans? How AI Represents Plans Using Hierarchical Task Networks
No full textFor an artificial intelligence to be able to solve real-world problems, it must be able to represent knowledge about not only the domain in which it is working, but also knowledge about its own approaches that it derives. When these approaches are complex enough to necessitate planning before any action is taken, the AI has to decompose the space of possible solutions into granular tasks and reassemble them into a structure forming the correct path. One method of accomplishing this is through implementing hierarchical task networks, where the network resembles a tree structure with the addition of preconditions allowing non-linear solution paths to be formed as necessary. This presentation will explain the benefits of this form of knowledge representation for planning and describe some of its successful applications in online safety.https://digitalcommons.jsu.edu/ce_jsustudentsymp_2024/1048/thumbnail.jp