ikatkov/journal-of-student-research-hs-articles huggingface dataset:
Efficacy of Techniques Used in the Medical Diagnosis and Treatment of Glioblastoma
Authors: Pranav Potluri; Jothsna Kethar
Date: 11-30-2022

Upon diagnosis, patients are presented with various treatment options from which to choose from. Oftentimes, however, the patient is oblivious to what exactly is being presented to them. The complexity of the medical field is unrivaled and patients are often blind to what is being done, especially in oncology. Oncology is regarded as an incredibly complex field with even more complex subgroups. One of society’s most perplexing cancers is Glioblastoma, a type of brain cancer in which a growth appears in the brain or spinal cord Scientists know very little about the disease as it affects an extremely small percentage of the population. However, it is important to be informed about the possible treatment options and specific details of each. The purpose of this research is to inform society of the treatment options for Glioblastoma and how effective they are at curing the disease. This journal elaborates on these complex treatments, ranging from the classic methods of radiation therapy to the intricate methods of brain surgery. Upon reading this paper, one will have a deeper understanding of what it means to undergo cancer treatment, especially for a sickness as dire as Glioblastoma. The paper will explain three techniques used to treat Glioblastoma including how the treatment is performed, under what circumstances will it be performed, and the effectiveness of the technique. These treatments are incredibly elaborate, but by congesting the main cancer treatment techniques into one, organized document, patients can better their understanding of Glioblastoma and its treatment options.
The Quarter-Life Crisis: The Lack of Identity Development Support in Adolescents
Authors: Kristina Chao
Date: 11-30-2022

The adolescent stage of development involves wide-scale neurological growth, thus allowing for a considerable emergence of mental health disorder symptoms, stemming from imbalances within the brain. Identity formation is a significant factor in adolescent maturation; however, rapid development can result in small-scale identity crises. Lack of identity development support in a teenager’s environment can lead to discrepancies between their self-concept and the societal image they present, otherwise known as identity regression. During the adolescent period of growth, teenagers evolve into fully-functioning members of society which is undoubtedly a critical stage that would benefit from identity development support. In order to prevent future difficulties, it is crucial to establish a nurturing foundation that is accepting of mistakes. Establishing healthy behaviors that are suitable to one’s environment can assist in identifying the vulnerabilities that arise within teenagers, in turn potentially avoiding many more severe mental disorder symptoms.&nbsp;
The Impact of Payday Loans on Healthcare Choices & Physical Health
Authors: Achinteya Jayaram; Hammad Shaikh
Date: 11-30-2022

Healthcare economics and payday loans have been a significant challenge for the United States population for many years, with various papers finding broad connections between them. This paper focuses on one such connection: how payday loans impact the overall physical health of an individual. I have analyzed data from the National Longitudinal Surveys of Youth and the Payday Loans and Customer Service data source from Kaggle, and estimated regression models in order to find correlations between variables. Using this, I have found a direct connection between payday loans and healthcare choices: if people take out payday loans, they are more likely to be in a better state of physical health. Also, taking out a payday loan does not necessarily result in a visit to the doctor's office. In additional analysis, I find that racial minorities are most likely to take out payday loans. Lastly, I have highlighted the need for higher transparency around the fees/interest charged on payday loans.  &nbsp;
Impacts of Climate Change on American Immigrant Flows and Employment Sectors
Authors: Ziang Li; Gabriela Nagle Alverio
Date: 11-30-2022

This paper focuses on the impacts of natural disasters caused by climate change on American immigrant flows and American immigrants’ employment sectors. This paper consists&nbsp;of&nbsp;two distinct quantitative analyses. The first analysis investigates the relationship between climate disasters and the number of people&nbsp;that&nbsp;immigrated to the U.S. from 2010 to 2019 (which is the closest intact decade before the pandemic) for several&nbsp;countries&nbsp;and shows a&nbsp;positive relationship between&nbsp;climate change and&nbsp;American immigrant flows. This part also discusses&nbsp;the inherent variation&nbsp;between&nbsp;developed countries and developing countries, and analyzes&nbsp;the relationship between the gross domestic production index and the number of people&nbsp;that&nbsp;immigrated to the U.S. In the second part of&nbsp;the&nbsp;analysis, the paper analyzes the&nbsp;several American states that contain the highest number of immigrants and which employment sectors immigrants preferred in 2015, which is the median&nbsp;year of the period in the first analysis. As a&nbsp;result, the result shows that American&nbsp;immigrants’&nbsp;employment sector&nbsp;distribution&nbsp;varies for&nbsp;each state, and&nbsp;American immigrants are more&nbsp;likely to participate&nbsp;in&nbsp;fundamental occupations such as agriculture&nbsp;and infrastructure&nbsp;construction. At the end of the paper, some viable suggestions on current immigration policy and future prospects in the related research fields are mentioned.
The Effect of the NIL on Recruitment
Authors: Mehul Arunarthi; Genevieve Gregorich
Date: 11-30-2022

The recent emergence of pay for collegiate athletes through the reformation of the NIL rules (name, image, and likeness), can change the entire system of recruitment of college sports, while will affect the entire business scope of the NCAA. I studied the subject of recruitment for the NCAA from the years 2018-2022 to analyze any changes in the recruitment plane before and after the NIL rule change in 2019. This research showed while a profound effect hasn’t taken place yet, changes are bound to come along, as the NIL rule becomes more widespread, and athletes start to take advantage of its benefits. These choices made by rising college athletes will alter the recruiting game as a whole as some schools will be targeted more than others.
Analysis of PyroTamer’s Effect on Current Wildfire Situations
Authors: Jaein Kim, Jermaine Lei, Matthew Lee, Daniel Cha
Date: 11-30-2022

Wildfires have been devastating Earth’s lands since its birth, but only recently have the fires become more rampant and increasing in numbers due to poor human decisions and excruciatingly worsening climate crisis. Our protectors, the firefighters, are constantly on an uphill battle to fight the numerous wildfires surrounding them during fire season, let alone risk their precious lives. To make matters worse, critical amounts of time are wasted as firefighters are still using old technology from decades ago like hand radios as they have been proven for a long time to be reliable. Although the reliability of hand radios is strong, they have extremely limited range and make communication onerous as verbal communication is not that efficient and prone to errors. PyroTamer is developing an application that focuses on reliability and efficiency to ensure firefighters are able to effectively communicate quickly in order to put out fires faster.
How Changes in Individual Spending Habits Under COVID-19 Impact SMEs in Sports Industry in Guangzhou
Authors: Zhengnan Liu; Cody Dodd
Date: 11-30-2022

Starting from 2020, life for everyone has shifted due to the unprecedented pandemic (Coronavirus, COVID-19), and so do SMEs (Small and Medium Enterprises). The purpose of the present study is to examine the relationship in what COVID impacts SMEs in Sport industry and whether structural changes have occurred for those firms due to the transformation of customers' spending habits. The study mainly focuses on a regional scale in Guangzhou, one of the cities in Guangdong Province, China. Five Interviews had conducted via phone call or personal meeting about the current situation of sport-related firms and their experiences about their past two years. Through those interviews, it can be concluded that SMEs in the Sports Industry have been devastating in the past two years, and most of them have to make structural changes to the structure of the firm to keep the firm functioning under current circumstances. Through past literatures, a social phenomenon has shown that people started to change their spending habits after COVID through reasons of anxiety and higher tension in their financial situation.
The Role of American NGOs in International Development
Authors: Emma Crasnitchi; Svetlana Crasnitchi
Date: 11-30-2022

In this paper, I will examine the role of American nonprofit NGOs - specifically D.C. located CNFA (Cultivating New Frontiers in Agriculture), Inc. - on the long-run input into agricultural growth and sustainability of third-world countries. Such NPOs render production successful. I surveyed various employees from CNFA to understand the commercial and humanitarian principles behind international development and collaboration with foreign agribusiness companies. I will describe the methods of helping food security, program development, and financing to reduce the cleavage of modern-day society.
Is Gene Therapy a Band-aid or a Cure?
Authors: Sanjitha Sadhneni; Rajagopal Appavu, Coach Jo
Date: 11-30-2022

The goal of innovation in treating diseases is to provide a long-lasting solution. For rare diseases such as sickle cell disease (SCD) and hemophilia, this can mean reducing the number of complications or even increasing life expectancy. One of the innovations that is having increasing attention is gene therapy. Gene therapy entails substituting flawed genes with normal ones by utilizing vectors derived from the outer shells of viruses, retaining the inherent properties of being able to target and enter specific cells. The modified gene is placed within this shell. Gene therapy can target germline or somatic cells - the latter being the most commonly used. The process for gene therapy is in-vivo or ex-vivo, depending on whether the transduction of the cells happens within or outside the body. Clinical trials in gene therapy have progressed tremendously, and even a few have reached approval by the FDA - but none yet for SCD or hemophilia. For both these diseases, the current treatments provide symptomatic relief but not long-lasting benefits. Currently, there are several gene therapy clinical trials ongoing for both conditions. This paper focuses on published results of sickle cell diseases and hemophilia and examines whether they are pointing towards short-term benefits or whether the effect is long-term.&nbsp;
Public Healthcare Policies for the Elderly in the United States: Suggestions from a Comparison between South Korea and the United States
Authors: Jooyoung Choi; Deepon Bhaumik
Date: 11-30-2022

As the aging population percentage rapidly increases across the world, leading to an increase in the necessity of long-term care, it is crucial for the government of the United States to implement changes to create more sustainable, and sufficient programs. This policy brief intends to identify problems in the current United States long-term health care system, and will try to find a possible suggestion to impede the gaps in the system. To bring a direct comparison and possible solutions, this brief will also investigate South Korea, a country with similar aging demographics and economic development as the United States. South Korea ranks 10th in GDP, and the elderly population comprise of 17.5% of the entire population (compared to 16% for the U.S). The South Korean examples of the policy suggest a few practical solutions to the issue, such as an increase in basing long term care eligibility on health status of an individual, rather than an emphasis on income eligibility. Targeted policies such as South Korea’s Alzheimer detection program should be more widely utilized for most chronic diseases in the United States. These types of prevention services will be able to help decrease the total amount of funding spent on these patients. Learning from South Korea’s policies can provide the U.S. with services that can adequately address the elderly population’s need for assistance and care.
Exploring Cryptocurrency: How do individual behaviors in the financial world affect the potential of economic bubbles?
Authors: Ren Leong; Stefano Benigni
Date: 11-30-2022

Cryptocurrency has made waves recently with its absurd increase in prices and volume. In this paper, I sought to understand, fundamentally, what factors could have helped its rise to the top. On the other hand, large ascents like this in the past have shown to be fatal, such as the housing market bubble of 2008. It struck me that cryptocurrency seemed to be heading down a similar path, especially with its unpredictable volatility and lack of practical usage in daily life. I explored some of the behavioral trends that are prevalent in finance, and used them to understand the 2008 bubble. I then further explored cryptocurrency behavior to draw comparisons, which were not lacking. By understanding what was behind cryptocurrency’s incredible rise and what behaviors and trends may factor into its volume, I was able to come to the conclusion that cryptocurrency is an asset class that treads dangerously along the lines of forming bubbles.
Military Conscription in the 21st Century: Obsolescence or Relevance?
Authors: Glenn Lee; Ja Kyung Han
Date: 11-30-2022

Post Russian invasion of Ukraine, questions about conscription have come into focus, prompting questions about military readiness and the value of such a policy. In analyzing the policy, international relations theories can act as a useful framework, and help elucidate conflict dynamics. In particular, Realism and Liberalism have contrasting depictions of the world, with the former characterizing it as an anarchic system in which states compete for power and security and the latter as a system in which states allay tensions through cooperation. This research draws upon these theories as it uses hypothesis testing—t-tests, ANOVA, and chi-squared statistics—to investigate the different attributes of countries as they relate to conscription policies. A wide range of variables were chosen including military expenditure, imports and exports, the Human Development Index, and the EIU Democracy Index in order to test statistically significant differences through the aforementioned tools.&nbsp;Overall, this research finds that both realist and liberal theories have explanatory power and provide insight into how desire for hard and soft power may impact decisions to enact and maintain conscription policies. Data and graphs were interpreted alongside such concepts in order to reach this conclusion.
Sustaining Green Efforts: How Tech Parks in Bangalore Address Environmental Crises
Authors: Arshia Mehra; Courtney
Date: 11-30-2022

Bangalore, the capital of Karnataka is known as the Silicon Valley of India for the high number of ICT(Information and Communications Technology) companies and tech parks it has. The ICT industry has providedmillions of people with jobs in this city and has resulted in a population explosion, which has over time led toBangalore facing infrastructural crises along with strain on its natural resources. This paper discusses howsustainable architecture can set up a model for sustainability in perpetuity in Bangalore’s tech parks in the 21 stcentury.
International Law and The Responsibility to Protect
Authors: Sophia Sultan; Christopher Michael Garrity
Date: 08-31-2022

In the UN, there is a doctrine called the Responsibility to Protect that allows the blue helmets from the UN to intervene in a country if a country violates international human rights laws. This doctrine was created to aid refugees and the international community when their own countries abuse power and, as a result, them. My question was what determines when the UN decides to intervene under R2P. My hypotheses theorized that GDP, nuclear weapons, or race demographics must influence the verdict of when the UN intervenes. I thought that countries that had a higher GDP probably had a lower chance of being intervened even if they had committed humanitarian crimes. I also figured that any country with nukes would have high unlikeliness to be intervened upon, and finally, I hypothesized that countries with a white majority population were less likely to be intervened in. I found that a combination of portions from all three hypotheses was ultimately correct. Of the countries I tested most countries that had been intervened in under R2P were poorer than countries that had not been protected by the UN despite being guilty of similar injustices. My second hypothesis was arguably arbitrary since no country that owns a nuke has been intervened in by the UN. Lastly, the countries I tested for their race demographics were mainly non-white countries, and, therefore, the countries that had and had not been intervened in under R2P were isolated from the race debate.&nbsp;
Analysis of Anxiety and Depression in the Context of Commercially-Available Energy Beverage Consumption
Authors: Nathan Ming; Chloe Cavanaugh
Date: 08-31-2022

Energy drinks are common in the diets of teenagers. Despite the increase in consumption in American teenagers, little has been done to study its effects on the anxiety and depressive behaviors of teenagers.&nbsp;25 participants were selected including a majority of Asians and Caucasians between the ages of 12 to 22. The participants filled out a survey that recorded a baseline for a week, and then drank increasing dosages of energy drinks the following week while continuing to fill out a survey. They filled out a survey that was designed with SIG E CAPS indicators on each day, and the results were compared using a student’s t-test.&nbsp;The results were not statistically significant, but this supports that the caffeine limit set by the Food and Drug Administration does indeed prevent negative effects of caffeine on adolescents. One participant, with a pre-existing psychiatric condition, withdrew from the study after his markers of anxiety were significantly higher in the week that he took the energy drinks.&nbsp;While the study was limited in both scale and dosage, it did fall in alignment with the body of literature that preceded it. To formulate a statistically significant study with results that have a higher confidence level, the dosage of caffeine and the number of participants who consume it would need to be increased.&nbsp;The study, while not being statistically significant, reinforced the pediatric limit of caffeine and opened the door to further studies that involve a higher dosage or more participants.&nbsp;
Prediction of Chronic Graft vs. Host Disease Using Machine Learning
Authors: Sanay Bordia; Professor Ramezani
Date: 08-31-2022

This paper attempts to predict the onset of chronic Graft vs. Host Disease (GVHD) in children with blood cancers who have received a bone marrow or stem cell transplant using machine learning models. It analyzes and compares the results of three different models in terms of how accurate they each are in predicting chronic GVHD. These models are Logistic Regression, J48 algorithm using decision trees, and Multilayer Perceptron. The models are formed using a dataset containing 36 attributes, excluding chronic GVHD itself. Through data preprocessing and analysis in Weka, these 36 attributes are narrowed down for each model to figure out which combination of attributes leads to the best predictive accuracy. The study uses 10-fold cross validation for each model and uses the Receiver Operating Characteristic (ROC) Area as a measure of the accuracy for each model. The study found that Multilayer Perceptron is the best predictor of chronic GVHD. In comparison, Logistic Regression was the worst predictor of chronic GVHD. The J48 algorithm used the least number of attributes to make its prediction.
Examining the predictive power of moving averages in the stock market
Authors: Arjun Chaddha; Shilpa Yadav
Date: 08-31-2022

Moving averages are common technical analysis tools which investors use to generate buy and sell calls in the stock market. The purpose of this research paper is to analyse whether common moving average techniques can reliably predict stock market behaviour. Using hypothesis testing, this paper tests whether the percentage return yielded by using moving average combinations to trade stocks in the S&amp;P 500 index was significantly higher than a) the percentage return yielded by randomly buying and selling the stocks and b) the market percentage return. The tests were conducted for the S&amp;P 500 stocks in four different time frames to understand the performance of moving averages during different stock market trends (uptrend, sideways trend, downtrend). Moreover, the performance of three different buying and selling techniques which use moving averages were compared. The results of the paper indicate that an investor should not use moving averages to trade stocks owing to their limited predictive power. There were only a few moving average combinations which were significantly better than randomly buying and selling. Even those few combinations could not yield higher percentage returns than the market percentage return.
Deep Neural Network Classifier for Alzheimer’s Disease  Omics biomarker prediction for early and quantitative Alzheimer's Disease diagnosis
Authors: Jason Lin; Dr. Hayan Lee, Dr. Michael Snyder
Date: 08-31-2022

Alzheimer's disease (AD) is a neurodegenerative disease characterized by dementia and, eventually, a loss of cognitive abilities. Two histopathological features are associated with AD, neurofibrillary tangles, and amyloid-beta plaque. Both contribute to neuron cell death, neuron dysfunction, and AD pathogenesis. Current methods to diagnose AD remain reliant on symptomatic diagnosis with interviews that can be time-consuming, costly, and inaccurate. Alternative methods such as brain imaging are expensive and require extensive laboratory setup for accurate results. Thus molecular-level quantitative approaches are necessary. Omics datasets and machine learning technology advancements have opened new avenues to diagnose AD. This paper proposes using statistical methods such as principal component analysis, t-distributed stochastic neighbor embedding, and Kolmogorov-Smirnov test combined with Benjamini-Hochberg correction through feature selection and dimensionality reduction to isolate significant features associated with AD. Furthermore, we developed machine learning models based on logistic regression, random forest classifier, and deep neural network (DNN) classifier to predict AD diagnosis. Eight unique genes (TGM2, NKIRAS1, SYK, GABARAPL2, ABCC12, NDEL1, TEP1) were identified as significant biomarkers of AD and confirmed previous works identifying prognoses' roles in AD. After extensive hyperparameter tuning, the DNN model showed the best prediction performance for AD diagnosis among the three machine learning algorithms. The DNN model and preprocessed dataset demonstrated a 5-fold cross-validation accuracy of 0.823 and AUC-ROC of 0.940. Its code is publicly available at https://www.kaggle.com/neobrando/ml-dnn.