The Relationship between Sun Protective Behavior, Health Beliefs, Attitudes, and Norms of Sun Exposure among College Athletes

The purpose of this study was to access the relationship between sun protective behavior, health beliefs, attitudes, and norms of sun exposure among college athletes. An electronic survey was provided to student athletes for about two weeks in October of 2023. Prior to student participation the Institutional Review Board approved the protocol and data collection. The survey was sent to the students by the athletic director through an app called Teamworks that connects to all the athletes at Coastal Carolina. The majority of students who took the survey reported they did not wear sunscreen in past games/practices. Of those who felt susceptible to skin cancer, believed their lifestyle increased their risk of skin cancer, and felt reapply sunscreen was not an inconvenience were more likely to wear sunscreen during their games/practices. Those who felt susceptible to skin cancer due to their lifestyle and who reported their friends wore SPF 30 sunscreen were more likely to get sunburnt at games/practices. This study further shows the importance of informing college athletes about sunscreen protection and skin cancer awareness. There is a need for more encouragement and information spread about this topic to have a greater chance at preventing skin cancer

Collaborating to Implement SeamlessAccess: A library’s perspective

In the past two years an ever-increasing number of publishers have implemented SeamlessAccess resulting in a better user experience and increased usage. However, to ensure more users benefit from federated authentication and seamless access more collaborations among publishers, libraries, and SeamlessAccess are needed. This brief paper will include a library perspective on the challenges to implementing federated access, the benefits that federated access brings to libraries and to their end-users in their research experience, and a brief walk-through of the SeamlessAccess experience that demonstrates how it enhances federated access

Discipline, Disparity, and Diplomas: Suspension and Grade Retention in a Southeastern State

This study was designed to explore how race and gender impact student success in a southeastern state. A mixed-methods design was implemented so that the quantitative data could be further explained and explored using qualitative research. The quantitative analysis was conducted using a three-factor ANOVA to analyze the number of days a student misses due to suspension and the number of grade level retentions; race, gender, and the district a student attends were used as the independent variables in the analyses. Purposive sampling and the development of a script for the qualitative interviews followed the quantitative analyses. Interviews with administrators in one district within the southeastern state were conducted to further explore the impact of race and gender on discipline and student grade level retention. The results of this study highlight the need for schools and districts to evaluate their discipline practices and explore the disparate number of males who are being retained in a grade level

A STUDY LINKING TITLE 1 STRATEGIC RESOURCE ALLOCATION TO STUDENT ACADEMIC SUCCESS IN SOUTH CAROLINA PUBLIC SCHOOLS

Strategic instructional resourcing has become an important topic in public education, the most lucrative and expensive business in the United States. It is estimated which the United States spends about $612.7 billion annually on public education. This study will analyze and synthesize data from Title I schools in South Carolina, specifically in the area of resource allocation and its relationship to student academic success. Historically, public education has provided all citizens with an equal and equitable opportunity to receive an adequate education. Over time, access to strong public education can impact the lives of students with regard to social mobility. Instructional leadership is a term which appeared during the 1970s through the research conducted by Ronald Edmonds. In his publications Edmonds found which school leaders who focused on learning in their actions and conversations throughout the school year had a deeper impact on student learning. This was a significant discovery because school leaders during this time focused more on inspiring students and stakeholders to work together towards a common goal. These leaders had qualities of what is referred to as being a “transformational leader”, which meant a leader devoted to reflecting on student data and what/how teachers teach. Prior to this point, teachers were regarded as the experts and the reason why students learned or did not learn. After this research was published, school leaders were determined to have the biggest impact on student learning. School leaders who were most effective had a strong focus on areas including: collective efficacy, evidence, implementation, learning, student engagement, and instructional strategies. This research study is designed to examine how Title I high schools in the state of South Carolina are resourcing their Title I funds. Studies have shown which schools which practice effective and efficient specific instructional resourcing improve their students’ academic success. This paper will also explore what steps schools execute before making strategic instructional resourcing decisions. Keywords: Funding, per-pupil expenditure, equity, quality education, social mobility, socioeconomic status, achievement gap, adequacy, Title I, instructional leadership, six principles of instructional leadership, strategic resourcing, instructional resourcing, English language learner (ELL), socioeconomic disadvantage (SED), special education (SPED), end of course tests (EOC), graduation rate, college and career readiness assessment

Applications of Artificial Intelligence to Improve Coastal Ocean Modeling

Numerical Modeling (NM) is widely used to simulate and predict hydrodynamic processes and marine particle movements in coastal oceans, particularly during extreme weather events and emergencies. NM offers the capability to realistically simulate multiple state variables and fill gaps caused by scarce observations. However, inherent uncertainties exist in all NMs, primarily arising from the following three factors: 1) insufficient observations leading to uncertain model initial and boundary conditions, 2) inevitable truncation errors due to coarse model resolution, and 3) imperfect physics parameterization schemes for sub-grid processes, especially those related to waves. The consequences of these uncertainties are that 1) even state-of-the-art NM methods can produce unsatisfactory marine particle movement predictions with marine particle trajectory errors growing rapidly over time, and 2) NM often fails to adequately represent wave-induced water turbulence mixing in predictions and simulations based on Eulerian and Lagrangian approaches. These uncertainties are difficult to address using traditional NM methods because of their inherent limitations. In this dissertation research, Artificial Intelligence (AI) models are utilized based on their capabilities of nonlinear solving to address the above-mentioned challenges. I hypothesize that AI can improve the accuracy of ocean NM. Two tasks are identified to validate our hypothesis: 1) developing an AI correction model to improve NM-predicted float trajectories and 2) developing an AI wave model as a substitute for wave NM to improve the representation of water turbulence mixing in ocean simulation to achieve more accurate results under hurricane scenario. I use Regional Ocean Modeling System (ROMS) model as the foundation to predict the float trajectory and simulate the oceanic hydrodynamics under hurricanes. Experiments of ROMS simulation will be conducted and the results compared with observations to evaluate the improvements in model accuracy achieved through the application of the developed AI-methods. In the task of AI correction for NM-predicted float trajectories, I designed an AI model that incorporates Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) modules. To train this AI model, I have utilized a dataset consisting of 4501 observed 1-day float trajectories obtained from the Ocean of Things (OoT) program. These observations serve as the ground truth in the AI model training. The corresponding ROMS-predicted float trajectories are utilized to create AI input dataset. This AI input dataset includes various parameters such as the latitudes and longitudes of ROMS-predicted float trajectories, water depth, time, wind velocity at 10 m above the sea surface, and sea surface current in the zonal and meridional components. I randomly selected 3601 out of the 4501 trajectories for training this AI correction model. The remaining 900 1-day float trajectories were used to validate the trained AI correction model. The results of this AI correction model indicate that 1) The AI correction model can effectively improve the ROMS-predicted float trajectories. At the 24th hour, approximately 82% ROMS predicted float trajectories in the test dataset are successfully corrected by the AI model, resulting in a 57% improvement in trajectory prediction accuracy. 2) The AI correction model also demonstrates its applicability under hurricanes. 77% of 75 ROMS-predicted float trajectories during the hurricane periods are improved by this AI correction model, further showcasing its effectiveness under extreme weather conditions. 3) The performance of the AI correction model varies depending on different conditions. In particular, the model’s performance was found to be lower in wintertime and nearshore regions, which can be attributed to insufficient training data available for these two scenarios, indicating that the model’s effectiveness could potentially be enhanced with more comprehensive and diverse training data. In the task of AI wave modeling for ocean simulation, I designed an AI model that combines the Bidirectional GRU (BiGRU) and Multi-Head Attention methods to emulate significant wave height (SWH), wave period, and wave direction of wind-generated waves. Additionally, a physics constraint between SWH and wave period is added into AI wave model to ensure the consistency between these two state variables. WAVEWATCH III (WW3) model-simulated and buoy-measured wind sea wave data are used to generate the AI ground truth datasets with the same data structures. WW3 model is a widely used and well-established numerical wave model that simulates ocean waves based on various inputs such as wind speed, atmospheric pressure, and bathymetry. It is extensively validated and calibrated using observed wave data from buoys, satellite measurements, and other sources. WW3 model outputs are considered to be a reliable representation of wave characteristics under specific environmental conditions. These model simulations undergo rigorous validation and comparison with observational data to ensure their accuracy and fidelity. As a result, the WW3 model outputs are often used as a reference or ground truth for evaluating and benchmarking other wave models, including AI-based wave models for the regions where the wave observations are not available. The integration of WW3-simulated and buoy-measured wave data can address the scarce spatial coverage of buoy observations and incorporate more real wave characteristics into WW3 simulation. The AI input dataset for training the AI wave model includes water depth, wind components in u- and v- directions at 10 m above the sea surface. In the AI training of wave direction, SWH and wave period are included as additional input data. The AI wave model is first pre-trained using the WW3-based dataset and subsequently re-trained using the buoy-based dataset. The performance of AI wave model indicates that 1) The WW3-buoy-based AI wave model demonstrated acceptable accuracies in the northwestern Atlantic Ocean under all weather conditions, with the RMSEs of 0.36 m for SWH, 1.08 s for wave period, and 32.89 deg for wave direction between the AI-predicted and buoy-measured waves. 2) The WW3-buoy-based AI wave model successfully emulates smooth and continuous wave data from coastal regions to open oceans, indicating that the AI model is able to capture the spatial variations of wave characteristics. 3) Under hurricane scenarios, the WW3-buoy-based AI wave model presents similar wind sea wave patterns to the Simulating Waves Nearshore Model (SWAN) wave model. Moreover, the AI model still maintains acceptable accuracies during hurricane periods, demonstrating its robustness and its ability to perform under extreme weather conditions. The validated WW3-buoy-based AI wave model is implemented to provide wind sea wave required for the turbulent mixing scheme in ROMS simulation under Hurricane Dorian (2019) and Typhoon Malakas (2016). The ROMS simulation results of these two tropical storms indicate that The AI wave model demonstrates the capability to replace high-demanding wave numerical models (e.g., SWAN and WW3) under hurricane scenarios for representing the wave effects on ocean simulation. Incorporating AI-derived wave data into ocean simulations can yield more robust and realistic results compared to ocean simulations that do not account for wave effects. The presence of waves significantly enhances water turbulence mixing and latent heat flux in the ROMS simulations. This effect leads to the generation of local cold wake areas with low sea surface temperature (SST). Waves play a crucial role in ocean dynamics by inducing mixing processes and impacting heat exchange at the ocean surface. Integration of AI wind sea wave cannot effectively optimize the performance of surface wind-wave (SWW) mixing scheme under Typhoon Malakas, compared to SWW mixing scheme with a default wave condition, which is attributed to the deficiency of SWW mixing scheme and no swell characteristics in current AI wave model

CHARACTERISTICS OF REFRACTIVITY AND SEA STATE IN THE MARINE ATMOSPHERIC SURFACE LAYER AND THEIR INFLUENCE ON X-BAND PROPAGATION

Predictions of environmental conditions within the marine atmospheric surface layer (MASL) are important to X-band radar system performance. Anomalous propagation occurs in conditions of non-standard atmospheric refractivity, driven by the virtually permanent presence of evaporation ducts (ED) in marine environments. Evaporation ducts are commonly characterized by the evaporation duct height (EDH), evaporation duct strength, and the gradients below the EDH, known as the evaporation duct curvature. Refractivity, and subsequent features, are estimated in the MASL primarily using four methods: in-situ measurements, numerical weather and surface layer modeling, boundary layer theory, and inversion methods. The existing refractivity estimation techniques often assume steady homogeneous conditions, and discrepancies between measured and simulated propagation predictions exist. These discrepancies could be attributed to the exclusion of turbulent fluctuations of the refractive index, exclusion of spatially heterogeneous refractive environments, and inaccurate characterization of the sea surface in propagation simulations. Due to the associated complexity and modeling challenges, unsteady inhomogeneous refractivity and rough sea surfaces are often omitted from simulations. This dissertation first investigates techniques for steady homogeneous refractivity and characterizes refractivity predictions using EDH and profile curvature, examining their effects on X-band propagation. Observed differences between techniques are explored with respect to prevailing meteorological conditions. Significant characteristics are then utilized in refractivity inversions for mean refractivity based-on point-to-point EM measurements. The inversions are compared to the other previously examined techniques. Differences between refractivity estimation methods are generally observed in relation to EDH, resulting in the largest variations in propagation, where most significant EDH discrepancies occur in stable conditions. Further, discrepancies among the refractivity estimation methods (in-situ, numerical models, theory, and inversion) when conditions are unstable and the mean EDH are similar, could be attributed to the neglect of spatial heterogeneity of EDH and turbulent fluctuations in the refractive index. To address this, a spectral-based turbulent refractive index fluctuation model (TRIF) is applied to emulate refractive index fluctuations. TRIF is verified against in-situ meteorological measurements and integrated with a heterogenous EDH model to estimate a comprehensive propagation environment. Lastly, a global sensitivity analysis is applied to evaluate the leading-order effects and non-linear interactions between the parameters of the comprehensive refractivity model and the sea surface in a parabolic wave equation propagation simulation under different atmospheric stability regimes (stable, neutral, and unstable). In neutral and stable regimes, mean evaporation duct characteristics (EDH and refractive gradients below the EDH) have the greatest impact on propagation, particularly beyond the geometric horizon. In unstable conditions, turbulence also plays a significant role. Regardless of atmospheric stability, forward scattering from the rough sea surface has a substantial effect on propagation predictions, especially within the lowest 10 m of the atmosphere

Improving the Mental Health Crisis in Georgetown County Through a Small Business Lens

The United Nations outlines 17 Sustainable Development Goals to facilitate the growth of the world in a sustainable manner. As an intern through the United Nations Youth Corps Program, I witnessed the importance of a small business giving back to its community and how visuals for marketing a business can have an effect on consumers. I researched Georgetown County\u27s mental health crisis and how low-income, food insecurity, and lack of access to health care in the area are all leading factors in the depletion of the community\u27s good health and well-being (United Nations Sustainable Development Goal #3). Within this presentation, my role as a Graphic Designer will support a business\u27 plan to aid those in need of mental health care. Within my role, I utilize design and branding strategies that advertise and promote the message around mental health with increasing effectiveness. Overall, the examination of the business Salty Mile and it\u27s plan, better supports the facilitation of mental health and well-being in the Georgetown area

Mercury Contamination in the Caribbean

One of the main food sources of the Caribbean Islands are whales and other marine species. Due to human interaction and pollution the marine food chain has become very polluted in mercury, bioaccumulating in the species that are high on the food chain. There are also several active volcanoes surrounding the islands, meaning that if and when these erupt, mercury is also released into the air via ash. When consumed at high concentrations mercury can be highly toxic to humans. We were able to analyze over 500 samples of hair, volcanic sediment, and whale tissue taken from the Caribbean Islands and the area around them at the Environmental Contaminants Lab at Harvard University. From these samples we were able to see the concentration of mercury in each samples and trap the mercury isotopes. From this data we will match the isotopes taken from the hair samples to the isotopes in the whale tissue and the sediment samples to see which is impacting the countries more. This research gives headway in exploring how pollution on a human and natural level can show the government and the communities that actions need to be taken for the health of the people

Get ready, Ladies : A Content Analysis on Women’s Representation in Superhero Films

Superhero and villain films are one of the most popular genres known to modern-day society. Viewers are intrigued by the way characters are portrayed through their dialogue, costumes, and surrounding effects. This study will take a deeper look into the representation of women in superhero films and compare its findings to traditional gender stereotypes. A content analysis will be conducted over a few films from the DC Universe including Suicide Squad, Birds of Prey, and Suicide Squad 2. The films were chosen to focus on their female characters and how they are portrayed through their appearances, dialogue, screen time, and more. The study will also incorporate several other factors to support its findings including the Feminist Film theory, Objectification Theory, Social Cognitive Theory, and the male gaze

Group Theory Structures in Bobbin Lace

Bobbin lace is a complex textile art that intricately weaves dozens of threads into a single piece. Upon examination, it becomes apparent why bobbin lace is able to be so elaborate while remaining structurally sound. This talk will discuss the group theory structures in bobbin lace by abstracting the textile to build a mathematical model as well as applications in pattern making through enumeration