The Potential of Aerogels for Orbital Debris Remediation

Abstract

The issue of orbital debris has become increasingly critical, particularly with the rapid expansion of satellite constellations like SpaceX’s Starlink and Amazon’s Project Kuiper. These initiatives are expected to significantly increase the number of CubeSats in Low-Earth Orbit (LEO), substantially raising the risk of orbital collisions. As Earth’s orbit becomes increasingly congested, the threat of Kessler syndrome—a cascade of collisions generating more debris—looms larger. To address this challenge, I propose a mission concept for orbital debris remediation utilizing aerogel collector satellites. Aerogels are ultralight, synthetic, microporous materials that have demonstrated their efficacy in capturing high-velocity particles, as evidenced by their success in NASA’s Stardust mission in 1999. Their unique microporous structure enables them to decelerate hypervelocity particles over a very short distance without fragmentation. This mission envisions deploying an emergency aerogel-based orbital debris collector in response to sudden debris surges, such as those caused by satellite collisions or anti-satellite missile tests. The proposed system involves a medium-sized, bus-shaped spacecraft equipped with a deployable aerogel shield. In its stowed configuration, the aerogel is compactly folded into a circular structure. Once in orbit, it unfolds to form a protective barrier positioned at the spacecraft’s front. In the event of a debris-generating collision in LEO, up to 10 of these spacecrafts would be launched using Rocket Lab’s Electron launch system. Once deployed, the satellites would navigate through the debris cloud, capturing as much material as possible. After approximately one year, the spacecraft would deorbit and re-enter Earth’s atmosphere, allowing the collected debris to burn up upon re-entry.

Leveraging AI Machine Learning Models to Predict and Enhance Long-Term Investment Efficiency

Abstract

The stock market is a very tedious, difficult, and rewarding area that many people all across the world use to invest. However, there are some issues. As time goes on, the stock market becomes forever increasing and, in return, more intricate and complex to figure out. Getting to know the markets takes long hours of researching and analyzing data from the past and applying it to modern-day norms. This is why only certain people are suited for this type of work. Accurate stock price predictions benefit investors and traders in many ways. It helps with increased efficiency in the trading hours and creates higher margins and profits for these people. By using accurate predictions, they can further be more confident in purchasing and selling, which overall makes their job easier and more positive than what is usually negative. The purpose of this study is to showcase AI being applied for economic and financial enhancements such as investing. As a team, we have found that using a bot that predicts, buys, and sells stocks greatly increases investments. We used a select amount of machine learning models that were trained and tested to get the most accurate predictions possible to use in our Bot. We found that using an accurate model increases profits for investments in different brands many times over. This implies that our research and methods work well and are not faulty, as they were tested across many different companies at unique amounts.

Green Energy Policies and the Energy Sector: An OLS and Fixed Effect Regression Approach to Economic Impact

Abstract

Abstract With climate change on the rise, causing harm all across the globe, it has become imperative, now more than ever, to transfer to green energy from nonrenewables to decrease global emissions. This paper will analyze the impacts of green energy and specific green energy policies on economic growth, determining the best green energy initiative that will work for countries with varying economic structures to minimize short-term impacts. We will analyze these energy sectors with an OLS regression, with GDP Growth as our dependent variable and the energy sector, fixed effects, and control groups as our x variables. Through this analysis, we found that nonrenewables significantly negatively impact economic growth, while renewable energy is more likely to impact GDP positively. We also found that subsidy programs are the best policies for countries to maintain economic growth. With these findings, we can further contribute towards creating a suitable course of action to safely create a green transition and reach net zero by 2050.

PREVENTION METHODS TO REDUCE ANTERIOR CRUCIATE LIGAMENT INJURIES

Abstract

Anterior Cruciate Ligament (ACL) injuries are more common among female athletes with 16% of them experiencing ACL injuries during their careers. These injuries can cause physical and mental stress for athletes and can potentially derail their careers. Despite various prevention strategies, no consensus exists on the most effective intervention. Therefore, gaining knowledge of the anatomy and physiology of the ACL is essential in finding effective intervention methods for preventing ACL injuries. This review examines four major prevention methods to reduce ACL injury risk, which include stabilizing the trunk during landing, preventing the medial movement of the knees, using the correct lower muscles for absorption, and using all the lower extremities equally. Studies show that implementing these techniques lowers the ACL tear rates. In addition to these techniques, future studies should also explore new methods to enhance injury prevention and improve athletic performance.

Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias

Abstract

This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews to reduce inherent bias, specifically sentiment bias. Traditional interviewers are often subject to several biases, including interviewer bias, social desirability effects, and confirmation bias. This leads to non-inclusive hiring practices and a less diverse workforce. This study further analyzes various AI interventions in the marketplace today, such as multimodal platforms and interactive candidate assessment tools, to gauge the current market usage of AI in early-stage recruitment. However, this paper aims to use a unique AI system developed to transcribe and analyze interview dynamics, emphasizing skill and knowledge over emotional sentiments. Results indicate that AI effectively minimizes sentimentdriven biases by 41.2%, suggesting its revolutionizing power in companies’ recruitment processes for improved equity and efficiency.

The Role Of Music Therapy In Facilitating Episodic Memory Recall In Alzheimer’s Disease Patients: A Review

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive functions such as memory. AD is the most common form of dementia among older adults, and scientists continue to search for the exact cause of this disorder. Recent research suggests that music has the potential to facilitate memory recall for AD patients, especially when music stimulates emotionally-connected memories. One method used to enhance memory recall is music therapy (MT), in which patients listen to music that stimulates their emotions and facilitates recall of emotion-associated episodic memories. Studies have also shown links between self-selected music and an increased frequency of the use of emotional words during memory recall. Moreover, neuroimaging studies on music and emotion suggest changes in activity in the anterior hippocampal formation, a brain area involved in music-evoked emotions that plays an important role in emotions, behavior, and recollection of past experiences. Through a review of existing literature and empirical studies, this paper explores how familiar music can stimulate recall of emotionally-connected episodic memories in AD patients. The findings of this paper can highlight MT as a useful and accessible tool that could assist AD patients with recall of autobiographical and episodic memories. The reason for this review is to examine MT’s ability to potentially enhance the quality of life for individuals with AD.

Glucocorticoids and Mitochondrial Dysfunction in Parkinson’s Disease

Abstract

Glucocorticoids are hormones secreted by the adrenal glands and are controlled by the hypothalamicpituitary- adrenal axis in response to stress. Excess secretion of glucocorticoids can have a significant effect on the development of Parkinson’s disease and have a major impact on its pathophysiology by directing mitochondrial function. Since Parkinson’s disease is linked to a multitude of environmental factors including physiological and psychological stress and currently has no definitive cure, understanding relevant biochemical pathways involving glucocorticoids could offer insight into novel approaches or interventions for therapeutics. This paper will investigate the mechanisms by which glucocorticoids play a role in the pathogenesis of Parkinson’s disease through mitochondrial dysfunction.

Strong Opponents: Overcoming Obstacles of Osteosarcoma Metastatic Adaptation in the Lungs

Abstract

Osteosarcoma (OS) five-year survival rates drop from approximately 70% to 20% when it has metastasized to the lungs. Thus, there is an urgent need to understand the processes that allow these cancer cells to metastasize. In particular, it is critical to identify how the premetastatic niche is formed and how the cancer cells adapt to the lung microenvironment. This review paper surveys the current literature on the mechanisms of this. It includes descriptions regarding the role of driver mutations, cancer-associated fibroblasts, extracellular matrix stiffness, extracellular signaling, and other mechanisms contributing to metastasis. OS tumor cells secrete the molecules CXCL14, TRIM66, and Tim-3 to generate alterations to the tumor microenvironment that facilitate lung metastasis through epithelial-to-mesenchymal transition and actomyosin contractility. The activity of the MAPK and Wnt signaling pathways have been shown to correlate with the dysregulation of necessary normal processes including cell apoptosis, proliferation, and tissue homeostasis. These mechanisms can help inform future therapies targeting these essential components of OS lung metastasis.

Has the introduction of cryptocurrency as institutional-grade assets decreased herding behavior?

Abstract

This study investigates the impact of institutional-grade assets, specifically the approval of the Bitcoin Exchange-Traded Fund (ETF), on herding behavior in the cryptocurrency market. The objective is to explore how the institutionalization of Bitcoin may decrease investor behavior, particularly the tendency to follow the crowd, which is a common phenomenon in financial markets. I hypothesize that introducing institutional-grade assets could reduce herding behavior, as institutional investors may bring more stability and rationality to the market. To analyze herding behavior, we employ two methodologies: the Cross-Sectional Standard Deviation (CSSD) and the Cross-Sectional Absolute Deviation (CSAD) methods. Our analysis covers 77 days before and after January 10, 2024, the date of the Bitcoin ETF approval. While our findings do not provide strong, concrete evidence of herding behavior, we observe some indications of a behavioral shift, suggesting a potential decrease in herding post-introduction of institutional-grade assets. However, it is essential to note that the regression coefficients are nonsignificant, which limits the strength of our claims regarding changes in herding behavior.

The Transformative Role of Gleevac on Chronic Myeloid Leukemia and Other Cancer Lineages

Abstract

Chronic myeloid leukemia (CML) was once considered a terminal diagnosis for patients. It is a rare cancer that makes up approximately 15 to 20% of all leukemia cases in the United States, with about 9,280 new cases estimated in 2024 (5,330 in men and 3,950 in women) [1]. However, in the UK, CML accounts for less than 1% of all new cancer diagnoses. The disease predominantly affects adults, with the average age of diagnosis being around 64 years, and nearly half of all cases occur in individuals aged 65 or older, the cases of CML in children being extremely rare. In 2024, the United States estimated approximately 1,280 deaths from CML [2, 3]. Before modern therapies were available, patients often endured the full progression of the disease, which typically resulted in a fatal outcome. However, the development of Gleevec (imatinib) during the early 2000s revolutionized CML treatment and marked the beginning of a new era in targeted therapy. This paper will explore Gleevec’s development, mechanism of action, and its initial application to CML as well as similarly working diseases. Moreover, Gleevec’s success has extended beyond just CML as it has proven effective in other cancers driven by similar molecular mechanisms, such as gastrointestinal stromal tumors (GIST), and Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL). This paper aims to discuss and explore Gleevec’s broader impact in treating these diseases, highlighting its role in advancing targeted cancer therapies.

Efficacy of Virtual Reality Therapy for Treating Psychological Disorders

Abstract

This literature review will explore the efficacy and use of virtual reality therapy (VRT), a new and upcoming form of therapy. This typically involves exposure therapy, though it may also be used for other programs useful to specific disorders. As it is a relatively new form of therapy, not much is currently known about the effectiveness of VRT. This paper reports some studies which have been done on the topic to understand the effectiveness of VRT. During the research process, most of the articles were found from Google Scholar using specific keywords such as “virtual reality therapy”, “phobias” or “exposure therapy”. We see that in many cases, VRT is as effective as cognitive-behavioral therapy, with a few exceptions. It is likely that VRT is actually more effective, though other factors in these studies may have contributed to skewed results, which brings the need for further studies to find better methods of experimentation. We start by summarizing evidence of the unique benefits of virtual reality therapy that are not afforded by more traditional approaches. We then summarize the emerging evidence of VRT efficacy for treatments and describe the circumstances in which it has been effective while also noting the need for further evidence to validate certain use cases. With an eye to the future, we also describe research examining VRT-associated changes in brain function and how this information can inform improvement of VRT while helping researchers and clinicians understand potential mechanisms of its efficacy. Finally, we provide an update on recent innovations in VRT. VRT holds great promise for the future, as it seems likely to be a competitor with other, traditional forms of therapy. Overall, this paper highlights both the limitations and the promise of existing research on VRT while also calling for further study in this timely and highly clinically-applicable area.

Predicting Instagram Post User Engagement With Machine Learning Models

Abstract

This study investigates the practical application of statistical machine learning techniques to predict average Instagram post user engagement based on an account’s follower count. It explores three distinct models—linear Regression, Random Forest Regression, and Neural Networks—for their effectiveness in modeling engagement patterns. Using recent Instagram data, each model is trained on the average user engagement for a profile. The predicted data is then compared to the actual data to determine the most accurate and viable model. The Neural Network excelled in capturing variance, having the highest R-squared value of the three models tested, but struggled with overfitting. Random Forest handled non-linear patterns well, having the lowest mean squared error out of the three models, but tended to overestimate. LASSO Regression was a balance between both the Neural Network and Random Forest Model, maintaining variance capture while reducing overestimation. Future research could refine models or explore hybrid approaches for better scalability. Machine learning shows promise in predicting post popularity, but further improvements are needed to aid social media creators and developers.

Investigating Apolipoprotein B as a Diagnostic Indicator and Therapeutic Target of Coronary Artery Disease within the South Asian Population

Abstract

Coronary Artery Disease (CAD) is one of the leading causes of mortality among South Asians, who represent only 25% of the global population but account for approximately 60% of CAD cases worldwide. This disparity is attributed to both genetic predispositions and lifestyle factors that contribute to an atypical lipoprotein profile, particularly characterized by elevated Apolipoprotein B (ApoB) levels. A systematic meta-analysis was conducted using six interventional studies published between 2004 and 2024, sourced from Google Scholar, PubMed, and Elicit, to evaluate the diagnostic and therapeutic potential of ApoB as a biomarker for CAD in South Asians. Results consistently demonstrated higher ApoB concentrations and ApoB mRNA expression in individuals with severe CAD. One study by Rychlik-Sych et al. reported that women requiring coronary artery bypass grafts had significantly elevated ApoB, while men exhibited a four-fold increase in ApoB gene expression. Additionally, research from the University of Kansas Medical Center identified specific APOA1 gene polymorphisms more prevalent in South Asians, contributing to lower HDL cholesterol levels and an increased ApoB/ ApoA1 ratio—a strong predictor of cardiovascular risk. Despite its predictive value, ApoB remains underutilized in clinical settings. The discussion emphasizes the limitations of conventional lipid panels and argues for the integration of Apolipoprotein-100 tests and targeted therapies to enhance early detection and intervention. Ultimately, the study advocates for a shift toward ethnicity-specific cardiovascular care, where the inclusion of ApoB as a core biomarker could significantly reduce CAD burden in South Asian communities and lead to more equitable health outcomes.

A Novel Implementation Of Large Language Model Based Turn-Taking Conversational Intelligent Assistance Technology For Seniors

Abstract

Seniors living alone or in nursing homes are often isolated from interpersonal interaction. Existing senior care Intelligent Assistance Technology (IAT) faces challenges, including a rigid conversational structure, a lack of proactive responses, and an inability to address interruptions in a timely fashion. To address these issues, I present a framework that aims to a) develop an Automatic Speech Recognition (ASR) Natural Language Processing (NLP) IAT conversational platform that can parse user speech, analyze speech sentiment, save speech content, and respond with a situationally appropriate tone and content and b) test and implement novel interruption detecting models to simulate authentic conversation. A variety of interruption detection methods were evaluated using the ASR-NLP IAT framework, including facial sentiment analysis, head direction tracking and pupil tracking. Final iterations of this turn-taking technology demonstrator involving facial sentiment analysis reached 84.6% accuracy and an F1 score of 0.6. In conclusion, it is proven that ASR-NLP IAT has matured to the phase where it can effectively simulate person-to-person conversation and fluidly exchange conversational roles.