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Investigating the effect of NaCl stress in Raphanus Sativus
No full textSalt stress is a significant challenge in food security. Factors such as erratic weather, fertilization practices, and irrigation practice have exacerbated the problem. Therefore, it is important to understand the effects of increased concentrations of salts in the soil that negatively affects plant health with cascading effect on human nutrition and health. For these reasons, this experiment investigated the impacts of NaCl stress on Raphanus sativus (var. Cherry Belle), which is more commonly known as radish. Radish was chosen as an experimental candidate due to their short life cycle, high nutrient content and close relation with members of other vegetables of Brassicaceae family. Several factors such as germination, growth, physiology, nutrient content, and gene expression were investigated in control and salt-stressed plants. The effect of different NaCl concentrations on germination of radish seeds were documented along with the effect of spiking with 100µM melatonin or 0.5 mM TEA or 5 mM EDTA. Through the germination experiment, it was discovered that germination declined as NaCl concentration increased, and this negative effect was compounded by the melatonin and EDTA treatments. TEA treatment had a positive effect in ameliorating NaCl stress. ICP-OES mineral analysis indicated increase in K, Na, and Al content of salt-stressed plants. Additional physiological analysis suggests that 100 mM NaCl treatment decreases relative water content of leaves of mature plants as compared to their counterparts that were not salt-stressed. Finally, RT-qPCR analysis revealed that NaCl stressed plants exhibited upregulation of SOD and PIP3 genes. The most notable complications discovered through this experimentation include excess accumulation of aluminum, diminished germination rates, and water loss for the plant. These findings also suggest that two mechanisms the plant may use to combat salt stress are upregulation of the SOD gene and the PIP3 gene. Future experiments including SynComs should investigate strategies to reduce the negative effect of salt stress and promote health of Raphanus sativus
Investigating the Effect of KCl stress in Raphanus sativus
No full textPresented by Stephanie Nelms at the Arkansas INBRE Conference on November 7-8, 2025
Quality Measures and Patient Outcomes
No full textAbstract
This quality improvement project explored the relationship between A1c monitoring frequency and unplanned hospital and emergency room visits in diabetic Medicare beneficiaries. A secondary analysis was conducted using a pre-existing de-identified dataset from a primary care clinic setting. A total of N= 2,853 Medicare with diabetes cases were included in this project from January 1, 2024, to December 31, 2024. Out of the N= 2,853 cases, there were n= 424 (14.8%) incidences of an unplanned hospital or emergency room admission, and of those n= 241 (56.8%) had occurrences of A1c monitored frequently, and n= 183 (43.2%) occurrences of A1c unmonitored. The results indicate a meaningful relationship between Medicare diabetes cases that frequently underwent A1c monitoring and hospital or emergency room admissions. This finding shows cases that received frequent monitoring likely have uncontrolled diabetes or other comorbidities, which increase the chances of admission. This project highlights the importance of implementing effective preventive care strategies to improve patient outcomes and diabetes management. Future studies should investigate strategies that enhance A1c monitoring and other diabetic indicators while reducing hospital and emergency room admissions.
Keywords: diabetes, A1c monitoring, hospital admissions rates, Medicare, quality measures, emergency room admission rates, preventative care, secondary analysi
An Iterative Approach to Utility Contribution for Arc Flash Incident Energy Calculations
Full text linkAccurately determining short-circuit fault contribution from utilities is a major challenge in arc flash incident energy analysis. Utilities may withhold or frequently alter data, and the presence of distributed generation complicates worst-case scenario identification. Additionally, commercially available software imposes limitations on scenario modeling and calculations, restricting engineers\u27 ability to refine results. This research introduces an iterative approach that enhances worst-case arc flash identification while addressing software constraints. Initially, the study applied this approach to various facility models using commercial tools, comparing different methodologies. However, the focus has since evolved into developing an open-source arc flash calculator. This tool surpasses existing software by enabling more detailed analysis, incorporating additional calculations, and allowing engineers to input custom protective device time-current curves (TCCs). Unlike proprietary solutions, this calculator provides transparency and flexibility, making it possible to refine incident energy estimates in cases where utility-provided fault data is unreliable. By integrating iterative analysis with an advanced open-source calculation platform, this project empowers engineers with greater control over arc flash modeling. The result is improved accuracy, adaptability, and accessibility in hazard evaluation, ensuring a more comprehensive approach to arc flash safety
The Hidden Cost of Intelligence: Environmental Impacts and the Rise of Green AI Solutions
Full text linkArtificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.
Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted by recent legislation in Virginia mandating water usage transparency. The demand for specialized hardware (GPUs, TPUs) accelerates mining activities in regions such as Africa and South America, exacerbating deforestation, pollution, and labor exploitation. Additionally, AI-driven consumerism, such as fast fashion brands like Shein, intensifies overproduction and waste, while hardware turnover contributes to millions of tons of e-waste projected by 2030.
In response to these challenges, the emergence of the Green AI movement presents a pathway to sustainable AI development. Companies like Google and Microsoft have committed to powering their data centers with 100% renewable energy by 2030, while Hugging Face promotes the reuse of pre-trained models and the development of efficient, low-energy AI systems. Researchers at the Allen Institute advocate for transparent reporting of energy usage, ensuring AI models are evaluated not only by accuracy but also by environmental cost. Governments are introducing regulations encouraging green practices, alongside efforts to develop recyclable AI hardware.
By highlighting real-world data and the rising adoption of Green AI practices, this study calls for urgent collective action—ensuring that AI continues to advance while aligning with global climate goals and resource sustainability
Power-Beaming: Wireless Energy Transfer using Lasers
Full text linkThe objective of the project is to implement a wireless energy transfer system utilizing laser technology. This system involves modulating the laser’s beam profile and spatial distribution through precise optical manipulation. The energy transmitted by the laser is captured by a photovoltaic array, commonly referred to as a solar panel, which is engineered to convert incident photons into direct current (DC) electrical power. The current challenge is to develop a reliable wireless energy transfer mechanism capable of operating over extended distances, thereby overcoming the constraints imposed by wired connections, particularly in environments where wiring is impractical, such as in space. My goal, however, is to apply this concept to a system that will convert the photons into electrical power to charge a capacitor which is then dispelled into a load. I am tasked with creating a system that can demonstrate power-beaming with high enough efficiency so that I can reliably charge a device. This technology holds promise for mitigating the environmental impact associated with disposable electrical components and the manufacturing processes involved in their production. Additionally, its application could extend to nanotechnologies and compact computing systems, where space constraints and rapid energy delivery are critical. This emerging technology represents a novel area of research with the potential to revolutionize energy transfer and utilization, opening avenues for further exploration and innovation in the field
Can Assessment of Morphometrics Correctly Predict Species in Madtoms?
Full text linkSpecies delineation is commonly achieved through genetic analyses, but the utility of morphometrics as an alternative method remains unclear. This study investigates whether morphometric measurements can effectively differentiate species in madtoms (Noturus spp.). A total of 10 morphometric traits were measured from 50 individuals each of three madtom species: the Slender Madtom (Noturus exilis), the Black River Madtom (Noturus maydeni), the Ozark Madtom (Noturus albater), the Ouachita Madtom (Noturus lachneri) and the Freckled Madtom (Noturus nocturnus). Random forest modeling was employed to identify which morphometric variables most accurately distinguish between these species. The findings suggest that morphometric data, when analyzed with advanced modeling techniques, can serve as a viable alternative for species delineation. This research provides valuable insights for future studies on species identification and enhances the utility of morphometrics in natural history collections, potentially reducing reliance on genetic methods in certain contexts
Impact of community gardening on neighborhood crime rates: Research proposal
Full text linkGardening has been a part of human existence from the beginning of time, but people have become distanced from gardening as civilization has grown and urbanized. As this distance has grown, so has the disconnect between people and the basic needs which shape their views of themselves and of society. It is possible that, in the drift away from gardening as communities, humans have become less moral.
The purpose of this study is to determine if community gardening lowers violent crime rates.
This relationship will be gauged by answering the following questions: Which type of community gardening benefits community relationships the most? Which type of community gardening impacts crime rates the most? How can parks and recreation agencies encourage community gardening?
In order to gather this information, control groups will be monitored over a 6-month growing period. These groups will consist of 3 different neighborhoods of similar economic classes and crime rates. There will be 3 levels of community gardening between these groups: no gardening, individual home gardeners, and individuals gardening on a community plot. The results of these case studies will help to determine how parks and recreation agencies can help to encourage community gardening for healthier communities
Cozy Bear Understanding Cyber Espionage Strategies Against the United States for Future Cybersecurity Preparedness
No full textRussian cyber-espionage groups have a focus on western-countries such as the United States and has multiple state-sponsored cyber espionage groups such as Advanced Persistent Threat (APT) 29, also known as Cozy Bear. Since 2008, Cozy Bear has spearheaded multiple high-profile, and destructive attacks against U.S. organizations and government This poster will analyze Cozy Bear’s cyberattack tactics, techniques, and procedures through U.S. based case studies and proposes recommendations on how to prepare for future cyber-attacks. This research paper will focus on five major incidents that occurred in the United States, the 2014 U.S. government email breaches, the 2016 Democratic National Committee (DNC) breach, 2020 cyber-attack on COVID-19 vaccine research, the 2020 SolarWinds Supply Chain Compromise, and the 2024 Microsoft Corporate email breach. Each case will go through the background of the attack, tactics and techniques used to breach the organization’s system(s). The attacks cause significant damage to organizations including intellectual theft, reputational damage, monetary losses, and compromised national security. Based on the similarities of strategies used in the various attacks the study will offer recommendations for preventing future attacks which includes cybersecurity training and awareness for employees, staying up to date threat intelligence, continuous threat monitoring, and enhancing identity and access management (IAM). Public and Private sectors in the United States must adopt proactive cybersecurity practices so they can effectively defend against Cozy Bear and other advanced persistent threats (APT)
