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Research Archive


Summit Young Scholars have been publishing their papers in journals and presenting at conferences since 2015, even during the COVID-19.

Below are abstracts taken from our past students’ research papers and posters. Our students write a full paper or make a poster. 

 

Humanities Abstracts

Detrimental Theoretic Analogies for Women in Popular Songs

Detrimental Theoretic Analogies for Women in Popular Songs

Ella K (New Jersey, USA)

Abstract 

This paper analyzes how conceptual metaphors in the lyrics of popular songs can be potentially detrimental by influencing the public’s perception of women. Conceptual metaphors in songs often compare women to objects (objects that are fragile and breakable or primarily of monetary value) and animals. Many common conceptual metaphors in music refer to women as less than sovereign, rational humans, implying that women should be owned, controlled, and used. These comparisons are harmful because music is so influential and has the ability to create and perpetuate stereotypes. By examining the lyrics of the popular songs: Bob Dylan’s “Just like a woman”, Robin Thicke’s “Blurred Lines” (written by Marvin Gaye), and Chris Brown’s “Fine China” we are able to discern subtle ways in which misogynistic language has become so imbedded into popular culture. 

Embracing Positive Masculinity: Fostering Healthy Notions and Confronting the Underrated Challenge

Kevin S (Connecticut, USA)

Abstract 

Toxic Masculinity, a term that refers to the extreme aspects of stereotypical masculine traits, has often been used by feminists and the general public to disfavor the overall male gender with too broad an understanding on the term of toxic masculinity and its origins. This study will discuss and explore the issue in four segments. Firstly, the term of toxic masculinity will be defined and its usage among the general public and other scholars will be explored.

Secondly, general psychological differences, particularly emotional regulation and expression tendencies, between males and females will be discussed in relation to perceived gender roles and stereotypes. Additionally, cultural and societal expectations assigned to men and their impact on perceived gender roles will be assessed, including their harmful/perpetuating effects on the mental health of men. Finally, a different perspective on masculinity, as being positive rather than toxic or harmful, will be evaluated.

Keywords: Toxic Masculinity, Feminists, Emotional Regulation, Stereotypes, Societal Impact

Grieving Melodies: Exploring Conceptual Metaphors in Songs Depicting Emotional Parting

Mary P (New Jersey, USA)

Abstract

This paper examines lyrics in contemporary popular music concerning emotional conflict in relationships, how metaphors and other figurative tropes are used in these songs to create greater meaning, and what they show about human cognition. It discusses conceptual metaphors as described by George Lakoff and Mark Johnson in Conceptual Metaphors in Everyday Language (1980) and their unusual complexity in song lyrics. The lyrics examined come from Little Talks (2011) by Monsters of Men, Fourth of July (2015) by Sufjan Stevens, and You’re Somebody Else (2017) by Flora Cash. All three songs concern separation from a loved one, by death or by emotional distance. The authors use conceptual metaphors in these songs to transform their negative feelings about these sad events into positive perspectives and describe their experiences of losing their loved ones in order to heal from their death or disappearance. The analysis identifies the conceptual metaphors, their entailments, and underlying cognitive patterns evoked by the lyrics. The results of the examination support Lakoff’s theory that conceptual metaphors subconsciously shape the way we think and act and share abstract experiences. It contributes further to conceptual metaphor theory by showing examples of poetically complex metaphors and how people may express difficult emotions through conceptual metaphors in order to cope or heal psychologically.

Keywords: conceptual metaphor, lyrics, psychological healing, loss, cognitive semantics

Examining Global Suicide Trends: A Socio-Cultural Analysis in the Context of Contemporary Events

Louis A (New Jersey, USA)

Abstract

We utilized country-level data on suicide rates from 1985 through 2015 provided by the WHO to explore global trends as well as country-specific trends. First, we find that up until 1995, there was an increase in suicide rates globally, followed by a steep decline in deaths. This observation is largely driven by the data from Europe, where suicides are prominent but steadily declining. Second, men are more likely to commit suicide than women across the world over the years. Third, the older generation is more likely to commit suicide than youth and adults. Finally, we turn to Durkheim’s theory and use it as a lens to understand trends in suicide across time and countries and attempt to identify social and economic events that might explain patterns that we observe.  For example, we discovered a drastically different pattern in suicide rates in the US, with a steep increase in suicides in the early 2000s. We hypothesize this might be driven by both the 9/11 attacks and the recession of 2008.

Navigating Mental Health: Exploring Immigrant Health-Seeking Behavior and Outcomes in the U.S. Amidst the Process of Assimilation

Lin K & Hailey W (New Jersey, USA)

Abstract

This research examines the intricate interplay between healthcare access and the mental health outcomes of immigrants in the United States. Immigrants in the U.S. experience the “Healthy Immigrant Effect,” where they initially experience better physical and mental health upon arrival but experience a decline the longer they stay in their new homes. This effect can be explained by examining immigrants’ health seeking behaviors. Health seeking behavior is part of the assimilation process for many immigrants, where they experience both enabling factors and barriers that are in part related to their cultural backgrounds as they navigate these new structural systems. In addition to the clear impacts on physical health, this paper argues that these health-seeking behaviors can also be related to the mental health challenges immigrants experience. Specifically, acculturation and internalized stigma emerge as significant factors influencing these behaviors in the healthcare realm. The acculturation process, involving adaptation to a new culture, can lead to identity conflicts and stressors when receiving care. Additionally, internalized prejudice and racism can damage self-esteem and mental health, preventing individuals from seeking care in the first place. By addressing these links, the U.S. can better support the physical and mental well-being of its growing immigrant population, harnessing the diverse talents and contributions that immigrants bring to the country.

Keywords: immigrant health, acculturation, mental health outcomes, health-seeking behavior, internalized stigma, diversity, healthcare access, healthcare systems, “Healthy Immigrant Effect”, assimilation

The Economic Scoreboard: Investigating the Global Business Impact of World Cup Soccer

Mike Y (Connecticut, USA)

Abstract

With over 3.5 billion fans and hundreds of millions of registered players globally, soccer (otherwise known as football) is the most popular sport in the world. Because of this, the soccer industry, which includes soccer events, like the FIFA World Cup, FIFA Women’s World Cup, and Olympic Games Football, as well as other soccer-related business, including merchandise sales and coaching, plays an important role in our global economy. In this study, key events and top organizations of the soccer industry are identified, and their economic roles in the development of nations, brands, organizations, and individuals are analyzed. Additionally, this study identifies the global soccer industry’s key sources of revenue and expenditures. This study is important and relevant for scholars or economists who are interested in sports economics as well as the future of sustainable economic development in sports.

Keywords: Soccer, FIFA, World Cup, Sports, Economic Impact, Input-output analysis, International Trade, International Business

Unmasking the Shadows: Investigating the Dark Side of Cyberbullying – Personality Traits and Predictive Patterns Among Adolescents and Adults

Clara K (Florida, USA)

Abstract

Cyberbullying is a common phenomenon that has only increased over the years. It has affected a wide demographic, but this paper primarily focuses on adolescents and adults. It will also explore how personality traits, specifically the Big Five and Dark Triad, impact being able to predict perpetrators and victims. The Big Five model is a foundational personality model commonly used in psychological research, which breaks down personalities into Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. On the other hand, the Dark Triad consists of the traits of Machiavellianism, Narcissism, and Psychopathy, which are typically associated with negative characteristics. It was found in many studies that many, if not all, of the Dark Triad had a positive correlation to cyberbullying, regardless of age. However, psychopathy and machiavellianism emerged as the stronger predictor of cyberbullying of the three.

Keywords: Cyberbullying, Personality Traits, Big Five, Dark Triad, Demographic Trends, Psychological Research

STEM Abstracts

Forecasting Financial Markets: A Comparative Analysis of Stock Price Prediction Using Facebook Prophet and ARIMA Methods

Forecasting Financial Markets: A Comparative Analysis of Stock Price Prediction Using Facebook Prophet and ARIMA Methods

Walter W (New Jersey, USA)

Automated forecasting of stock prices and trends is an active area of interest in machine learning. A variety of time-series statistical and machine learning based forecasting techniques have been developed over the years. In this work, we compare and evaluate the performance of a recently forecasting technique, Prophet, developed by Facebook for predicting stock trends and prices. For Comparison, we applied the Prophet and a traditional auto-regressive integrated moving average (ARIMA) model to predict the patterns of five stocks: Apple, Tesla, Facebook, Netflix, and Google. The performance of the two methods was compared using the root-mean square error (RMSE) statistic. The Results show that the Prophet method provides more accurate predictions (RMSE: 181.2) compared to the ARIMA method (RMSE: 52467.7). We have implemented a Python-based tool for stock prediction and forecasting using the Prophet and ARIMA methods.

Harmony in Sentiments: Crafting a Song Recommendation System through Sentiment Analysis of User Reviews

Andy K (New Jersey, USA)

Sentiment analysis and recommendation systems are among the most active areas of research in machine learning. Sentiment analysis focuses on using natural language processing and text mining techniques to evaluate the sentiment (i.e. positive, negative, neutral) of an unstructured text such as a tweet, comment, or product review. Recommendation systems use a variety of data science techniques to generate personalized content recommendations for the users. Here, we present a Python-based prototype for recommending songs to the users based on the sentiment of their reviews. We used the Amazon reviews and the Spotify music datasets from Kaggle for development purposes.

Streamlining Efficiency: Automating Wristband Extraction with Opentrons in Protocol Development

Thomas N (California, USA)

To better characterize the relationship between complex chemical exposures and disease, our laboratory uses an approach that combines low-cost, polydimethylsiloxane (silicone) wristband samplers that absorb many of the chemicals we are exposed to with untargeted high-resolution mass spectrometry (HRMS) to characterize 1000’s of chemicals at a time. In studies with human populations, these wristbands can provide an important measure of our environment; however, there is a need to use this approach in large cohorts to study exposures associated with disease. To facilitate the use of silicone samplers in large scale population studies, the goal of this research project was to establish automated sample preparation methods that improve throughput, robustness, and scalability of analytical methods for silicone wristbands. Using the Opentron OT2 automated liquid platform, which provides a low-cost and opensource framework for automated pipetting, we created two separate workflows that translate the manual wristband preparation method to a fully automated protocol that requires minor intervention by the operator. These protocols include a sequence generation step, which defines the location of all plates and labware according to user-specified settings, and a transfer protocol that includes all necessary instrument parameters and instructions for automated solvent extraction of wristband samplers. These protocols were written in Python and uploaded to Github (https://github.com/teikimm307/wristbandautomated ) for use by others in the research community. Results from this project show it is possible to establish automated and opensource methods for preparation of silicone wristband samplers to support profiling of many environmental exposures. Ongoing studies include deployment in longitudinal cohort studies to investigate the relationship between personal chemical exposure and disease.

Automated Dataset Generation for Event Reasoning: Crafting Interchangeable Steps for Seamless Analysis

John K (California, USA)

 

Actions, like cook chicken, change tire, and fold paper, have start and end states. Furthermore, a single action starting in a particular state can have multiple possible end states, some of which are unacceptable. For example, boiling breaded chicken is less tasty than frying it. Training models to understand which actions are swappable with each other—which actions can be exchanged with each other and still produce a possible and acceptable end state—would allow them to reason about the relationship between the start state and the action an entity goes through. Models that understand how to evaluate the swappability of two steps can better understand, modify, and generate procedural text, like instructions. To make progress toward understanding procedural text, we focus on recipes as a simplified subset with abundant online resources. We present a seed query database containing possibly swappable steps for recipes and a generalizable library to automatically generate new datasets from other instruction-based text. Our method clusters SentenceTransformers embeddings created from recipe titles so that recipes with similar end states are grouped together. Inside these clusters, we generate possible replacements from recipe steps that share a marginal amount of similarity. From a subset of 600 recipes and 50 clusters, we are able to generate approximately 80,000 queries. Our library is a stepping stone for the future development of models that are capable of modifying instructions without changing final results.

Assessing Machine Learning’s Prognostic Prowess: Predicting Mortality in Intensive Care Unit Patients through Method Performance Evaluation

Aiden C (Wisconsin, USA)

Abstract

Machine learning methods are increasingly being used for building diagnostic models in clinical settings to identify patients who are at a higher risk of mortality. Recent studies have shown that ensemble tree-based learning methods provide an alternative non-parametric approach compared to traditional methods for building predictive models in high-dimensional datasets. In this study, we evaluated the performance of logistic regression, random forest, XGBoost, and LGBM (leaf-wise tree-based learning algorithm) for identifying ICU patients with a 28-day mortality risk at the time of hospital admission. The case study data originates from a subset of publicly available data from the Medical Information Mart for Intensive Care (MIMIC) II database. The performance of different methods was evaluated using prediction error curves. The results show that the XGBoost classification method achieved the best prediction accuracy for classifying survivors vs. non-survivors with (cross-validation area under the curve; AUC=0.86). The top features for predicting death at the time of ICU admission included age, simplified acute physiology score (SAPS), and serum sodium levels at admission. These results can help predict which patients are likely to die within 28 days of ICU admission so that healthcare professionals can design & implement optimal treatment strategies to improve patient outcomes. All analyses were conducted using the AutoAI tool in IBM Watson Studio.

Keywords: Machine learning, mortality, predictive modeling, icu

Algorithmic Insights: A Comprehensive Evaluation of Machine Learning Performance in Stock Forecasting

Arthur S (Virginia, USA)

Abstract

A variety of time-series statistical and machine learning based forecasting techniques have been developed over the years which can be used for predicted stock prices and trends. In this work, we compare and evaluate the performance of auto-regressive integrated moving average (ARIMA) and Facebook’s Prophet time-series forecasting techniques to predict the future trends of eleven randomly selected stocks. The performance of the two methods was compared using the root-mean square error (RMSE) statistic. The results show that the ARIMA method provides more accurate predictions (average RMSE: 27.4) compared to the Prophet method (RMSE: 39.3). Additionally, we compared the predictions from ARIMA and Prophet with TipRanks, an AI based web application which gives future predictions for stocks. On average, the percent difference between ARIMA and TipRanks was 9.4% compared to 11.1% for Prophet vs TipRanks. We have implemented a Python-based tool for stock prediction and forecasting using the Prophet and ARIMA methods. 

Keywords: Machine Learning, Forecasting Techniques, Stocks, Trends

Unveiling Complexity: Applying Multifractal Detrended Fluctuation Analysis to Understand the Dynamics of the COVID-19 Pandemic

Emma P (Virginia, USA)

Abstract

In the context of infectious disease data analysis, the application of multifractal analysis, particularly Multifractal Detrended Fluctuation Analysis (MF-DFA), is explored, with a primary focus on understanding the COVID-19 pandemic. Daily case data from six countries is examined to unveil fractal behavior characterized by power-law relationships, offering valuable insights into the dynamics of disease transmission across various spatial and temporal scales. MF-DFA is introduced as a potent tool for analyzing nonstationary time series data, showcasing its ability to capture the intricacies inherent in natural processes. The study includes the computation of Local Hurst Exponents (Ht) at varying time scales, shedding light on local variations within the data. Additionally, the investigation of q-order Root Mean Square and q-order Hurst Exponents provides deeper insights into diverse aspects of data variability. This research underscores the multifractal nature of infectious disease data, emphasizing the importance of multifractal analysis in revealing nuanced patterns and correlations within complex time series data.

Creative Writing Archive

Essay & Poem

Item #1

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