| 1 |
Author(s):
Davy van Wingerden.
Page No : 1-8
|
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.
| 2 |
Author(s):
Pranav Prasath.
Page No : 9-12
|
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.
| 3 |
Author(s):
Aaroosh Shanker Reddy.
Page No : 13-25
|
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.
| 4 |
Author(s):
Sarvesh Sankar Dass.
Page No : 26-32
|
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.
| 5 |
Author(s):
Nishka Lal, Omar Benkraouda.
Page No : 33-39
|
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.
| 6 |
Author(s):
Bryan Im.
Page No : 40-50
|
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.
