| 1 |
Author(s):
Angela Chen.
Page No : 1-11
|
Music, Mental Health, and Mood Regulation: A Data-Driven Approach to Understanding the Role of Music in Emotional Well-being
Abstract
Mental health disorders, such as anxiety, depression, and insomnia, have become increasingly prevalent. This paper explores the relationship between mental health conditions and music-related factors using a dataset which includes variables such as music genres and listening hours. The study employs two types of statistical models—ordinal logit regression and count regression—to investigate the impact of music genres, listening habits, and demographic variables on mental health and music consumption patterns.
In the ordinal logit analysis, key predictors, including classical music preferences and favored genres, were positively associated with improvements in mental health, while listening to music while working and certain genres, such as R&B, negatively affected mental health outcomes. The count-oriented regression models, both negative binomial and Poisson models, used to assess the factors influencing daily music listening time, revealed that listening while working and being a composer were strongly associated with increased listening time, while being an instrumentalist decreased it. Psychological conditions like obsessive-compulsive disorder and insomnia were positively correlated with music listening hours, suggesting a potential coping mechanism for these conditions. Overall, this study provides valuable insights into how music preferences, mental health conditions, and listening behaviors interact, offering evidence-based recommendations for integrating music therapy into mental health treatments.
| 2 |
Author(s):
Earl Yin.
Page No : 12-22
|
Evaluation of Imputation Methods for Handling Missing Data in Mechanical Materials Datasets
Abstract
Mechanical design materials are integral to advancing modern technologies due to their diverse mechanical properties. These properties are crucial in determining the material’s suitability for various engineering applications. However, research on mechanical materials often encounters missing data, which can lead to biased results and reduced statistical power. While several imputation methods exist to handle missing data, there is a lack of focused studies evaluating their performance in the context of mechanical materials. To address this gap, a comprehensive dataset was obtained, and 10% of the original data for ultimate tensile strength (Su) and yield strength (Sy) were intentionally deleted. Four imputation methods—mean imputation, random fill, regression imputation, and k-nearest neighbors (KNN) imputation—were employed to restore the missing data. The performance of these methods was evaluated using Pearson’s correlation, multiple linear regression, and permutation feature importance. The results showed that mean and KNN imputation methods provided the closest match to the original data, while regression imputation also performed well with minor deviations. Random fill was the least reliable method. These findings provide guidance on selecting appropriate imputation techniques for mechanical materials datasets, ultimately improving the robustness of future research.
| 3 |
Author(s):
Elizabeth Pak.
Page No : 23-28
|
Food Processing and Its Effects on Allergenicity of Food Allergens
Abstract
A food allergy is a medical condition in which the body’s immune system overreacts to a specific protein in an ingested allergen. When the allergen is ingested and the protein is released in the blood, the protein binds to antibody Immunoglobulin E (IgE), causing a wide array of uncomfortable and painful symptoms.
Food allergies can be treated in a variety of ways, including the administration of antihistamines and, most recently, immunotherapy. Immunotherapy relies on exposing the patient to slightly growing increments of an allergen over time to build immunological tolerance against the allergen. Despite certain successes, neither antihistamines nor immunotherapy are foolproof treatments.
Food processing, including thermal and chemical treatments, is now considered a safer new approach to overcome food allergies. These treatments reduce the allergenicity of some food, but did not render the food entirely non-allergic. Food treatment, combined with other approaches of treatment, opens a new avenue for safer, more convenient, and more efficient food allergy handling. In this review, we will discuss the available methods of food treatment and how this affects the nature of the allergens in the food.
| 4 |
Author(s):
Varun Pothamsetti.
Page No : 29-33
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Reducing Drag by Optimizing the Underbody with Ride Height in Formula 1
Abstract
This study investigates the aerodynamic interaction between wheel wakes and the underbody
of Formula 1 cars, focusing on the effect of varying ride heights on drag reduction and ground
effect optimization. With the reintroduction of ground effect principles in the 2022-2026 Formula 1
regulations, the potential for enhanced vehicle performance through improved aerodynamic design is
significant. However, the efficiency of venturi tunnels, essential for generating effective downforce,
is compromised by turbulent airflows produced by the wheels, known as wheel wakes. This research
utilizes computational fluid dynamics (CFD) simulations to model these interactions at different
ride heights, aiming to pinpoint optimal configurations that minimize aerodynamic drag while
maximizing downforce. Initial findings suggest a delicate balance between ride height adjustment
and tunnel geometry optimization, offering potential pathways to achieve aerodynamic efficiency in
modern Formula 1 vehicles. This paper contributes to the evolving discourse on high-speed vehicle
aerodynamics, providing insights that could inform future vehicle design and regulatory frameworks
in motorsports.
| 5 |
Author(s):
Aarav Narang.
Page No : 34-40
|
The Promising Connection Between Gut Microbiota and Mental Health: Overview of Kefir in Relation to Mental Health
Abstract
Kefir, along with other fermented foods, have health benefiting traits and properties towards the gut microbiota. There continues to be much to discover about the microbiota as new research indicates that it connects with the brain. There have been numerous studies done on kefir’s impact on mental health showing that kefir could potentially treat mental health symptoms. However, some studies contradict that claim indicating that kefir does not affect mental health. Anxiety, depression, attention-deficit/hyperactivity disorder (ADHD), stress, among others, were the mental health issues explored in these studies. Presently, there is not enough evidence to make a feasible claim that kefir can improve mental health. However, promising results from kefir are possible as new research indicates that kefir could impact mental health positively.
| 6 |
Author(s):
Christina Feng.
Page No : 41-50
|
Determination of the Relationship Between the Flexibility, Materials and Sizes of Neural Probes and Their Effect on the Foreign Body Response
Abstract
Neural probes greatly assist us in our journey of learning about the brain by revealing the
functioning of neurons. Probes are implanted in different areas of the brain to record data or to
stimulate those sites. This detection and intervention of neural activity allows probes to diagnose
and treat diseases. Probe technologies have advanced over time and opened new avenues in neural
engineering. However, the appearance of a foreign object in the brain elicits a negative reaction that
harms the brain and its components. To minimize this reaction, scientists utilize a variety of methods
and designs that have yet to be perfected. This review aims to highlight the influence of neural
probe designs on the foreign body response. This includes looking at existing types of probes and
exploring their potential. The effects of different flexibilities, materials and sizes of neural probes
will be examined using studies and research made by scientists to decipher what methods minimize
the negative response the most. This paper also aims to identify the gaps in our current knowledge
of this subject.
| 7 |
Author(s):
Ella Fox, Brittany Katz.
Page No : 51-60
|
The Interplay between Disordered Sleep and Alzheimer’s Disease: Exploring the Bidirectional Relationship
Abstract
The debilitating neurodegenerative condition referred to as Alzheimer’s Disease (AD), results in
severe cognitive deterioration, including deficits in memory, confusion, and abnormal behaviour.
Sleep plays a crucial role in sustaining cognitive health, as it supports brain repair and performs
memory consolidation. Complaints of consistently disrupted sleeping habits, such as reduced quality
and fragmented sleep, are increasingly recognized as potential risk factors for AD. The fundamental
elements of healthy sleep: its structure, oscillations and benefits- then considering how changes in sleep
architecture, including alterations in REM and NREM stages, are associated with cognitive decline.
Another significant concept is the finding that sleep deprivation is often correlated with increased
levels of amyloid beta (Aβ) and tau proteins in the brain, which are two biomarkers of AD pathology
and accumulate with disease progression. This review examines the connection between unhealthy
sleep patterns during aging and AD development, emphasizing the need for continued research into
how improving sleep hygiene could potentially moderate the effects of cognitive deterioration.
| 8 |
Author(s):
Christopher Shu.
Page No : 61-65
|
Effect of Myrosinase Enzyme Encoding Gene Knockout on the Bitter Taste of Broccoli: A CRISPR-Cas9 Experimental Proposal
Abstract
Many people perceive bitterness when consuming broccoli. Glucosinolates and their degraded
products are the main contributors to the bitter taste. Production and hydrolysis of glucosinolates
require c gene using CRISPR-Cas9 will reduce broccoli’s bitter taste. This study also hypothesized that
knocking out the myrosinase encoding gene will reduce bitterness in broccoli. The research involves
designing a plasmid to deliver the CRISPR-Cas9 system to target and disrupt broccoli’s myrosinase
enzyme encoding gene. Successful completion of this proposal could lead to the development of a
broccoli variety that is more palatable to a broader population, potentially increasing its consumption
and associated health benefits.
| 9 |
Author(s):
Aaron Bedi, .
Page No : 66-71
|
Efficacy of Platelet-Rich-Plasma in Accelerating Muscle Injury Recovery in Athletes: A Literature Review
Abstract
Muscle strains are the most common injury, leading to enormous amounts of time lost for training
and participation in competitions, with long recovery periods and compromising the season play,
especially when seniors are involved. The treatment of muscle injuries includes the traditional use of
rest and ice compressions, but also involves the addition of physical therapy to improve the patient’s
condition and to help the rehabilitation of the acquired trauma. These injuries occur when muscle
fibers are overstretched or torn, resulting in pain, swelling, and limited movement, which can impact
both individual performance and overall team success. As a response to the limitations of conventional
therapies, platelet-rich plasma (PRP) therapy has emerged as a promising treatment option, involving
the injection of a concentrated solution of platelets derived from the patient’s own blood into the
injured area, in order to promote tissue healing and regeneration. While some studies suggest that
PRP injections can lead to faster recovery times compared to traditional treatments, others indicate
no significant benefits. This highlights the variability in outcomes influenced by factors such as injury
type and PRP preparation methods. This literature review critically examines the current evidence
regarding the efficacy of PRP injections in reducing recovery time for muscle injuries in athletes,
aiming to provide a comprehensive understanding of its potential benefits and limitations, and to
explore the hypothesis of whether PRP injections can effectively accelerate recovery for athletes with
muscle strains and sports injuries.
| 10 |
Author(s):
Nitya Sarvesha.
Page No : 72-81
|
Clinical Applications of Liquid Biopsy in Women with High Risk of Developing Breast Cancer
Abstract
Breast cancer currently has effective detection and treatment processes, but there are still many aspects that the process lacks which causes breast cancer to be in need of more accurate tools. Currently, liquid biopsies have shown great promise in the future of detection and management of cancer. Liquid biopsies are able to be noninvasive, performed more frequently than a single biopsy, and able to gain a comprehensive understanding of the entire tumor through a single blood test. However, liquid biopsies need to be more sensitive and accurate in its detection to be implemented in the future. We conducted a search for relevant papers that gave current and future understandings on the implementations of liquid biopsy in women with early and late, metastatic stages of breast cancer. Through these studies and papers, most studies suggest that higher sensitivities will only be improved with multi-modeled assays by using multiple biomarkers to help to increase sensitivity and improve reliability of diagnosis. This gives hope for liquid biopsies to be continued to be developed for progression on cancer detection for women with high risk in breast cancer with the use of multiple biomarkers.
| 11 |
Author(s):
Adeev Mardia.
Page No : 82-96
|
Predicting Bitcoin’s Price Evolution: A Comparative Analysis of Meta – Learning Algorithms
Abstract
This manuscript presents a comparative study of three machine learning models—Long Short-Term Memory (LSTM), Random Forest Regressor (RFR), and Support Vector Machine (SVM)—for predicting Bitcoin closing prices over the period from 2015 to 2023. The study focuses on evaluating the predictive performance of these models in terms of key metrics such as Mean Squared Error (MSE) and R-squared values, which measure their ability to forecast Bitcoin’s price trends.
The findings reveal that the Random Forest Regressor (RFR) model outperforms both LSTM and SVM in terms of accuracy and robustness. RFR demonstrated the lowest MSE and the highest R-squared value, effectively capturing both short-term fluctuations and long-term trends in Bitcoin prices. LSTM exhibited moderate performance, struggling to capture extreme volatility, while SVM showed the poorest results, with the highest MSE and lowest R-squared value.
In addition, this study explores the relationship between Bitcoin’s closing prices and trading volume, identifying a significant correlation that provides insights into market sentiment and price volatility. Challenges such as data preprocessing for SVM and hyperparameter tuning for LSTM are also discussed. The results underscore the potential of machine learning, particularly Random Forest, in enhancing cryptocurrency trading strategies and risk management. Future research will focus on integrating additional features and optimising models for real-time applications.
| 12 |
Author(s):
Akshita Vattikuti.
Page No : 97-104
|
Ethical Perspective of The Usage of Genetic Editing For Cosmetic Purposes
Abstract
Gene editing is a new and upcoming technology used to alter the genetic makeup of organisms.
Due to its popularity, gene editing technologies come with many ethical debates, social norms, and
beliefs that analyze their importance and usage. This paper will explore the potential implementation
of genetic engineering in the cosmetic industry by analyzing professional opinions and past
implementations. The paper examines current cosmetic procedures like in vitro fertilization and
plastic surgery, before delving into the ethical debates surrounding designer babies and germline
editing for cosmetic-related traits. Some key risks identified and discussed include off-target effects,
the complexity of the gene network, and the potential unintended consequences of altering pleiotropic
genes. The paper also discusses recent advancements in prediction methods, such as in silico tools,
aimed at reducing the risks associated with gene editing. Through a synthesis of numerous arguments,
the use of this up-and-coming technology in cosmetics was deemed too risky in our current state but
with more research and careful oversight, it could be a possibility in the future.
| 13 |
Author(s):
Saaya Accapadi.
Page No : 105-109
|
Gender Differences in College Admissions Essays
Abstract
This study aims to understand how gender influences the writing style of students applying to
college. It uses the variationist approach to sociolinguistics to collect and analyze data in order to
demonstrate a difference between the way male and female students use language in their college
essays. All the essays analyzed come from the book 50 Successful Swarthmore Application Essays by
William Han and Sean Cheng (2020), which contains application essays by students who come from
many globally and economically diverse backgrounds. Results show that male applicants had a higher
rate of personal pronoun usage while female students had a higher rate of hedging within their essays.
| 14 |
Author(s):
Ryan Verma.
Page No : 110-115
|
A Comparison of B Cell-Targeting Treatments for Multiple Sclerosis
Abstract
Multiple sclerosis (MS) is the most common chronic inflammatory neurodegenerative disease and a leading cause of nontraumatic neurological disability in young adults. The cause of MS is not fully understood, though certain genetic and environmental factors are believed to play a role. B cells are an important part of the immune response and have been shown to play a significant role in MS. This paper reviews studies on various B cell-targeting treatments for MS to determine which are the most effective at specific stages of the disease. In the end, all of the treatments that were investigated were found to be effective for treating relapsing-remitting MS, and results varied for primary progressive and secondary progressive MS.
| 15 |
Author(s):
Nyshita Chalasani.
Page No : 116-120
|
Impact of Valproic Acid Exposure on CNTNAP2 Expression in Autism Development
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by both genetic
and environmental factors. The Contactin-associated protein-like 2 (CNTNAP2) gene, critical for
neural development, has been associated with ASD, particularly when its expression is disrupted.
Prenatal exposure to valproic acid (VPA), a teratogenic anticonvulsant, furthers this risk by altering
CNTNAP2 expression during pregnancy. This review explores how CNTNAP2 function and VPA
exposure interact to influence ASD development, with an emphasis on interactions between the
environment and genetics. Potential therapeutic approaches, including gene therapy, HDAC inhibitors,
and dietary interventions, offer hope for these effects. However, there are gaps in research, particularly
in long-term outcomes and personalized treatments. This paper demonstrates the importance for
targeted therapies addressing the intersection between genetics and environmental factors.
| 16 |
Author(s):
Mahima Sanghera.
Page No : 121-128
|
The Effectiveness of Pharmacological Treatments for Major Depressive Disorder
Abstract
Across all demographics, major depressive disorder (MDD) continues to rank first globally as the most common and often incurable mental health condition. In particular, this study identifies two categories: conventional antidepressants and rapid-acting antidepressants. It analyzes selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) from conventional antidepressants, as well as ketamine and esketamine from rapid-acting antidepressants. Utilizing existing research, this review examines the similarities and differences in the brain region affected by each treatment through neurotransmitters based on the type of antidepressant. The purpose of this review is to compare and contrast how varying antidepressant treatments affect neurotransmitters and how this determines their onset and duration of efficiency.
| 17 |
Author(s):
Ruohan Huang.
Page No : 129-148
|
Adapt to Win: A Statistical Analysis on Strategies in Rock-Paper-Scissors
Abstract
Game theory provides a robust framework for analyzing strategic decision-making, and Rock-Paper-Scissors (RPS), despite its simplicity, serves as an effective model due to its balanced structure and broad applicability. This study evaluates the performance of nine RPS strategies—ranging from random and fixed patterns to adaptive techniques—through pairwise matchups simulated over multiple rounds. Statistical tools, including Moving Average, Cumulative Sum (CUSUM) Control Chart, and Decay-Weighted Metrics, assess each strategy's stability and adaptability in individual match pairs and overall outcomes. Results show that adaptive strategies focusing on recent history, such as multi-round observation and certain reaction-based techniques, excel in long-term performance by precisely adapting to opponents' behavioral shifts while avoiding overreactions to minor variations. These findings highlight the critical importance of adaptability in optimizing decision-making in competitive environments.
| 18 |
Author(s):
Aneira Vundecode.
Page No : 149-157
|
Clean Air, Clear Minds: A Real-Time Carbon Dioxide and Volatile Organic Compounds Monitoring System for Classrooms
Abstract
Indoor air quality (IAQ) in educational environments significantly impacts student health and academic performance. This research presents an electronic system for monitoring IAQ, focusing on detecting carbon dioxide (CO2) and volatile organic compounds (VOCs) using a microcontroller-based prototype. Inspired by previous research linking poor IAQ with student performance, this system was developed to measure and analyze these parameters for use in educational facilities. The proposed system utilizes commercially available Arduino-based sensors and components, enabling real-time data collection and analysis. Preliminary results indicate that the system detects changes in CO2 and VOCs levels in real-time. The system was designed to wirelessly send real-time sensor readings to an online dashboard, which allows for sharing the data to the cloud. The data can be internally shared with the members of an institution to allow for intervention if necessary. This research emphasizes sensor calibration and validation, ensuring the system’s readiness for real-world classroom settings, and offering a practical solution to improve IAQ in educational environments.
| 19 |
Author(s):
Ruoqing Huang.
Page No : 158-174
|
Uncovering Audio Features Shaping Popularity in Chart-Topping Songs: A Statistical Approach
Abstract
This study examines how audio features influence the popularity of songs on Spotify, focusing on tracks that have appeared on the Billboard Year-End Hot 100 charts. Using data from both Billboard and Spotify, the analysis explores the relationship between features such as Energy, Danceability, Loudness, and Instrumentalness and their impact on Spotify's Popularity scores. Correlation and regression models, ensemble learning, and clustering techniques were applied to uncover patterns and insights. Results show that high-energy, danceable, and loud songs tend to achieve higher popularity, while quieter and more experimental tracks are often less favored. Dimensionality reduction and clustering methods identified groups of songs with distinct audio profiles, highlighting the characteristics associated with varying levels of engagement. This research provides insights into the role of audio features in shaping song popularity, offering useful information for artists, producers, and industry professionals.
| 20 |
Author(s):
Khushi Agarwal.
Page No : 175-182
|
Training AI to detect Tuberculosis in chest X-rays
Abstract
Tuberculosis (TB) remains a significant global health challenge, leading to over 1.3 million deaths in 2022. Early and accurate detection of TB is crucial for effective treatment. Shortage of expert radiologists, especially in resource-constrained settings is one of the most significant impediments in detection of TB. This study explores the application of artificial intelligence in detecting TB features in chest X-rays. Utilizing a data set of 4,200 labeled chest X-ray images, a convolutional neural network (CNN) was developed to identify key radiological features associated with TB, including infiltrates, cavities, pleural effusion, enlarged lymph nodes, and miliary patterns. The model achieved an accuracy of 97.98%, demonstrating its potential as a supplementary tool for healthcare professionals. However, limitations such as reliance on a homogeneous dataset and lack of integration of patient medical history suggest further enhancements are necessary. Future work should focus on diversifying the dataset and incorporating comprehensive diagnostic elements to improve the model's applicability in varied clinical scenarios.
| 21 |
Author(s):
Ibrahim Irfan.
Page No : 183-189
|
Comparing simple neural network and ensemble learning models in predicting hydration energy of molecules represented by RDKit and Mordred descriptors
Abstract
Predicting molecular properties is a crucial task in the chemical sciences. Recently, there has been an enormous focus on leveraging machine learning to predict various molecular properties ranging from solubility to reaction rates. This study develops machine learning models to predict the hydration energy of molecules, comparing the performance of a neural network (Multi-Layer Perceptron) and an ensemble learning model (Random Forest). Using descriptors generated by RDKit and Mordred, we aimed to identify the optimal molecular representations for predictive accuracy. The FreeSolv database of 642 molecules provided experimental hydration energy data for training and testing. The models were evaluated using mean squared error (MSE) and the coefficient of determination (R²), with the Multi-Layer Perceptron achieving an R² above 0.9, outperforming the Random Forest model. Results suggest that the neural network model, in combination with RDKit descriptors, offers a strong balance between accuracy and computational efficiency. This study demonstrates the potential for simpler machine learning models to accurately predict molecular properties, supporting broader applications in chemistry where computational resources are limited.
| 22 |
Author(s):
Oliver Chi.
Page No : 190-196
|
The War of 1812 and the Legacy of Nationalism
Abstract
This article primarily explores the impact of nationalism on the causes and legacy of the War of 1812, and how it informed later historical events. Nationalism, as a desire to avenge both real and perceived slights such as the impressment of American sailors, British participation in the Northwest Indian Wars, and the perception of disregard on the part of the United Kingdom for American independence, functioned first and foremost as a motivator for the United States to declare war. After the war's end, it became a lens through which the public in both the United States and Canada could regard the conflict and frame themselves as victors before it ultimately faded from public memory. Politically, nationalism helped provide a sense of unity to the emerging republic, and helped solidify the dominance of the Democratic-Republican Party in national politics. Culturally, the war provided several important symbols that emphasized national unity, which have lasted to the modern day despite the obscurity of the war in which they originated.
| 23 |
Author(s):
Rou-Syuan Huang (Rosalyn Huang).
Page No : 197-205
|
In what ways does celebrity worship impact the social and emotional well-being of teenage fangirls in East Asia?
Abstract
This paper explores the impacts of celebrity worship on teenage fangirls, emphasizing how the phenomenon of celebrity idol influence shapes the mental well-being, social interactions, and daily lives of fangirls aged 10 to 19 in East Asia. Data was collected through journal articles, essays, and online interviews with 11 adolescent girls from Taiwan. The findings suggest parasocial relationships can provide emotional support, boost confidence, and create a sense of community for fangirls. However, they have the potential to affect time management negatively, create unrealistic relationship expectations, increase emotional vulnerability, and contribute to self-doubt. This research contributes to a broader understanding of the relationship between media, culture, and adolescent development.