Liberty University

Food insecurity affects people across the United States, particularly college students. Among college students, rates of food insecurity are consistently higher than the national average, and disparities exist between students at historically Black colleges and universities (HBCUs) and students at predominantly white institutions (PWIs). Researchers have evaluated the influence of food environments on food insecurity. This study seeks to compare the food environments surrounding HBCUs and PWIs, as represented by University of South Carolina (USC) campuses, in South Carolina, USA. DatabaseUSA was used to determine the restaurants and retail food stores around these universities. This data was analyzed to establish the existence of food deserts and food swamps, as well as calculate the Modified Retail Food Environment Index (mRFEI) for each campus. The results show statistically significant differences between HBCUs and USC campuses for the number of healthy food stores within 1 mile (p = 0.03) and the distance to the nearest healthy food store (p = 0.03). The results show 62.5

This study investigates financial development and economic growth and moderated for the role of national security, environmental sustainability and green bonds in the emerging economies using annual time series data which covered the period of 2000 to 2022, panel differenced and system generalized method of moment (GMM) as the baseline model as well as fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) as the models for robustness checks. Financial development was stratified into financial development-financial institution access index and financial institution depths index and financial markets-measured with financial markets access index and financial markets depths index. Also, national security were measured with military expenditure, global peace index and global terrorism index, environmental sustainability was measured with indicators such as ecological footprint, biodiversity index and renewable energy share, while the green bonds were measured with environmental protection expenditure and carbon footprint of bank loans as we controlled for climate change and foreign remittances. Findings from the GMM results revealed that while financial development and green bonds shows positive and significant effects on the economic growth in the emerging economies, the national security had negative effects, while the environmental sustainability had significant negative and positive effects on the economic growth of the emerging economies and similar findings were made from the results of the robustness checks (FMOLS) and (DOLS). Also, from the results of the interactive effects financial development with national security, environmental sustainability green bonds, we found that the variables had significant effects on the economic growth. In addition, findings from the marginal effects (ME) and threshold effects (TH) results revealed that while financial development improves economic growth, additional effects of national security, environmental sustainability and green bonds may have adverse effects on the economic growth in the emerging economies at a certain threshold. Based on these findings, this study recommends that financial development, national security and environmental sustainability should be improved by the authorities to forestall economic growth in the emerging economies.

Fluid simulations are cost-effective and zero-waste alternatives for research and development of polymer reactors. However, many polymer-specific simulation software packages assume a homogeneous reactant mixture, overly simplifying the physics. Computational Fluid Dynamics (CFD) simulations provide more insight into this process but are difficult to utilize with detailed chemistry mechanisms. This work seeks to verify the implementation of free radical polymerization chemistry into a plant-scale CFD reactor model for Low-Density Polyethylene (LDPE) to investigate spatial gradients and their impact on the system. The process resulted in the most accurate CFD polymer reactor simulation to date, known to the authors, and sets a precedent for the verification and validation of other reactor models. It was found that the axial flow profiles formed distinct regions within the reactor wherein there were unmixed polymer properties. Significantly more variability was also found in the upstream-most reactor zone compared to a downstream zone, exemplified by a coefficient of variation of 447% in the former and 0.372% in the latter for the polydispersity index on a central plane of the reactor. Additionally, noticeable differences in properties were found between the inside and outside of the mixing shaft, which were only fractions of a percent different.

Effective, low-cost water treatment approaches are needed to address increasing heavy metal contamination, particularly in the developing world. This study evaluates the use of fly ash, a waste byproduct from coal combustion, as a sustainable adsorbent to remove cadmium (Cd2+), cobalt (Co2+), and lead (Pb2+) from aqueous solutions. Two types of fly ash sourced from different power plants in the southeastern United States were evaluated in this study: Belews Creek fly ash (BFA), which was untreated, and Wateree Station fly ash (WFA), which was processed using a proprietary carbon burnout method. The removal kinetics followed the pseudo-second order model, with equilibrium being achieved within 2 h. The experimental data best fit the Freundlich isotherm model, compared with the Linear isotherm model, suggesting that multilayer adsorption occurred on the fly ash surfaces. The results showed that Pb2+ exhibited the highest removal efficiency for each adsorbent (BFA: 99%; WFA: >99%), followed by Co2+ (BFA: 98%; WFA: 94%) and Cd2+ (BFA: 93%; WFA: 56%). Removal efficiency was influenced by pH and was negatively impacted by increasing ionic strength and total organic carbon content. The findings demonstrate that fly ash is a viable, low-cost alternative for heavy metal removal for use in resource-limited areas.

Using machine learning to accelerate the characterization and prediction of properties of many-molecule systems, such as polymers, is appealing, yet challenging. Polymers are large, complex molecules that have unique properties and potential applications in a wide range of industries. Their potential in advancing fields such as ion-transport polymer for energy storage, lightweighting of structural materials, bioinspired multifunctional materials, etc., provide enough impetus for accelerating the discovery of novel polymeric materials. However, mathematical mapping and the consequent manipulation of polymer structures are still challenging tasks due to their complex configuration and the smorgasbord of motifs encountered naturally and in engineering materials. Traditional methods of polymer structure mapping and property prediction at multiscale domains can include approaches such as Density Functional Theory, Molecular Dynamics, and Finite Element Analysis, which can be time-consuming and computationally expensive. The promise of machine learning to accelerate these tasks is appealing, and currently, researchers are pursuing the development of architectures and composition approaches to accomplish this. Here we discuss the current state of the knowledge on the use of Graph Neural Networks, and related architectures, being developed and/or used for the characterization and prediction of properties of polymers. Many challenges still exist such as the lack of sufficient and comprehensive data sets. To address these issues, efforts are being pursued─such as the so-called CRIPT (Community Resource for Innovation in Polymer Technology) led by a lab consortium that includes representations from private industry, academia, government, and others. We conclude that even though this field is young it has both momentum and promise. The current challenges that must be overcome are also addressed.