ikatkov/journal-of-student-research-hs-articles huggingface dataset:
A Multi-Modality Magnetic Resonance Imaging Model for Predicting Traumatic Brain Injury Outcomes
Authors: Shruti Vadlakonda; Dr. Raymond Koehler, Janine Sharbaugh
Date: 11-30-2023

Traumatic Brain Injury (TBI) is a heterogenous injury and a leading cause of long-term deficits and mortality in the United States. In order to improve TBI outcomes, an effective prognostication tool is necessary. Standard imaging modalities, computerized tomography (CT) and magnetic resonance imaging (MRI), have a limited ability to predict TBI outcomes. Currently, advanced MRI techniques are being studied for their efficacy. The aim of this study is to determine whether a multimodality MRI approach is superior to a single modality MRI approach in determining clinical outcomes of TBI. A secondary data analysis was conducted on TBI data obtained from 31 rat brains; 3-day MRI data in the Ipsilateral Perilesion Cortex and 28-day Behavioral Test data (Novel Object Recognition, Barnes Maze, and Open Field Test Total Distance and Total Act Time) were analyzed. A Best Subset Analysis was conducted for each of the behavioral tests. Three out of four behavioral tests show improved adjusted R2 values for models containing more than one imaging modality. A Multiple Linear Regression Analysis was then conducted on the MRIs from the highest predictive model determined by Best Subset Analysis. This analysis shows that a multimodality MRI approach can explain 25.2% of the variability in behavioral outcomes in the Novel Object Recognition Test with a P value of 0.012. Thus, the study demonstrates that a multi-modality MRI approach has a potential for effectively diagnosing and predicting TBI outcomes.
The Bracero Program: A Catalyst for Social Justice
Authors: Teddy Fleiss; Michele Santosuosso
Date: 11-30-2023

This paper gives a background of the Bracero Program, a little known guestworker program that allowed millions of Mexicans into the United States. The Bracero Program had large-scale impacts, ranging from intertwining Mexican and American economies to impacting social justice movements regarding immigration reform and civil rights. Today, the program holds responsibility for strong links between Mexican and American communities.
The Role of Central Banks in Managing Monetary Policy and its Effects on the Economy  An Empirical Analysis of Policy Tools, Economic Indicators, and Implications
Authors: Atharva Singh
Date: 11-30-2023

This paper examines the crucial role of central banks in managing monetary policy and its profound effects on the economy. It provides an in-depth analysis of the objectives, tools, and strategies employed by central banks to regulate monetary conditions, control inflation, and promote economic stability. Additionally, it explores the transmission mechanisms through which monetary policy decisions impact various sectors of the economy, including interest rates, exchange rates, investment, consumption, and employment. The paper also highlights the challenges and limitations faced by central banks in conducting effective monetary policy, particularly in an era of economic globalization and financial interconnectedness. Overall, this research contributes to a comprehensive understanding of the intricate relationship between central bank actions, monetary policy, and macroeconomic outcomes.
A Supervised Deep Learning Model for the Detection of Cardiovascular Disease
Authors: Ananya Saridena, Abhaya Saridena; Jothsna Kethar
Date: 11-30-2023

In our world today, cardiovascular disease (CVD) stands as the foremost cause of death worldwide, claiming the lives of nearly 20 million individuals annually. As CVD continues to burden the healthcare industry, there is a critical need for early detection and prevention. The rise of Artificial Intelligence in the medical field offers a range of capable solutions. In order to address the problem, this paper presents the development of a simple, supervised deep learning model for detecting cardiovascular disease in patients. The research focused on creating a model enriched with multiple layers, activation functions, optimizers, and loss functions. The chosen approach leveraged the power of AI to analyze labeled patient data and map input features to corresponding class labels, enabling accurate detection of CVD. The dataset used contained 70,000 patient records with 12 different clinical attributes. In addition, it provides an overview of the most common types of cardiovascular disease, such as coronary artery disease, aortic valve disease, stroke and peripheral artery disease. The accuracy of the obtained results from the deep learning model was up to 73%. The utilization of AI systems can present a novel approach to addressing daily challenges within the rapidly-evolving world of medicine. Health personnel can take advantage of rapidly changing artificial intelligence and user friendly deep learning models to detect similar future medical concerns.
The Handover of 1997 and Its Effects on the Hong Kong Aviation Industry
Authors: Gareth Fung; Suraj Nair
Date: 11-30-2023

This paper explains the logistics behind the Handover Hong Kong on July 1st, 1997— an event transition Hong Kong from a sovereign British state to the special administrative region status of the People’s Republic of China. More importantly, it propounds that the Handover of Hong Kong had an indirect but nevertheless negative effect on Hong Kong’s Aviation Industry— most notably affecting the leading Hong Kong airline and air transport company Cathay Pacific. It explores this idea through a critical secondary literature review— intended to analyse the events occurring in Hong Kong prior and during the year 1997 alongside its effects on the industry. Furthermore, employing a case study design on Cathay Pacific enables the research paper to carry out a more structural analysis of the company’s performances in the late 20th century. It takes a macro-to-micro approach for its structure; first explaining the general trends and resilience of the global aviation sector, through developing an analytical lens. Then it uses Cathay Pacific’s performance to evidence the generalised theories. Overall, Hong Kong’s aviation industry saw only slightly deviated results to both empirical and theoretical behaviours of global aviation and air transport airlines following events often causing what is known as a career shock: a disruptive and extraordinary event caused by factors outside an individual’s control.&nbsp;&nbsp;
Music as a Language  Assessing the Extent to Which Active Music Therapy Promotes Socialization Development for Children Under 12 with Down Syndrome
Authors: Xinzhi Zhang
Date: 11-30-2023

The number of children in western countries diagnosed with Down syndrome (DS), a disease caused by chromosomal abnormalities, is still increasing. The resulting delayed cognitive development also leads to deficits in social functioning in children under 12. Active music therapy (MT), as a natural intervention to achieve therapeutic functions through improvisation, performance, and singing, has been proven to promote the socialization development of children with DS. To get a better idea of the extent of the promotion and exactly what aspects it has improved, I conducted literature reviews and interviews with a music therapist. The results suggest that active MT can stimulate the socialization development of children under 12 with DS in three aspects: language skills, social-emotional development, and prosocial behavior. Although the improvement effect of active MT is better than that of passive MT, it requires the client to have a basis of music theory, that is, it requires higher requirements on the client, so the treatment method should be selected according to the severity of the client's DS or a combination of the two. Hence, future research could be aimed at finding the simplest interventions with sufficient client engagement and verifying the continuation of the effect of active MT.
Exploring the Search Behavior of Teenagers: A Comparative Study of Social Media and Browser Usage
Authors: Midori Wang; Stephen Lind
Date: 11-30-2023

As technology advances, the use of social media is becoming more extensive, and many businesses are adapting their marketing plans to take advantage of it. Research has shown that individuals, especially teenagers, appear to be increasingly relying on social platforms like Tiktok as their primary search engine. This study aims to determine whether there is a growing number of people who use social media as their search engine. An online survey was distributed to several high school students who were asked to search for answers to questions across six different categories. The results indicate that most teenagers still use browsers and search engines like Safari, Google, or Chrome for information gathering due to their content diversity, quality, and habit. However, there was a wide variety of social media and other platforms used throughout the research, which demonstrates the potential of social media as a search engine.
Antibacterial Properties of Manuka Honey and the Role of Methylglyoxal
Authors: Youlin Feng
Date: 11-30-2023

 The unique ecosystems of New Zealand have produced a diverse range of honey over the years, with Manuka honey being one of the most renowned. Produced by Western honeybees extracting nectar from Manuka flowers, this monofloral honey has become known for its distinct antibacterial and anti-inflammatory properties. Whilst antibacterial activity in other honey tend to stem from factors such as hydrogen peroxide content, high viscosity, osmotic effect, and acidic pH, the antibacterial activity of Manuka honey is mainly attributed to methylglyoxal (MGO), a dicarbonyl compound which is found in high concentrations in Manuka honey. This review paper will focus on the antibacterial properties of Manuka honey and the role that MGO plays. Understanding the specific chemical mechanisms that of attack on different strains of bacteria by Manuka honey and the role of MGO is crucial to potentially understanding how new drugs or medicines can combat antibacterial resistance to antibiotics.
Investigating The Performance and Viability of Semiconducting Graphene as A Substitute for Silicon in Field Effect Transistors
Authors: Andrew Jiang
Date: 11-30-2023

This paper investigates the electrical and thermal properties of two competing methods of semiconducting graphene, doped graphene and reduced graphene oxide, and their respective viabilities for implementation in field effect transistors. We used the Graphene Field Effect Transistor (GFET) simulation software on nanohub.com to determine the current, voltage, and max temperature based on variations in the channel length, top gate oxide thickness, electron mobility, and thermal conductivity and then compared these performance results with those of a traditional silicon-based transistor. Our results demonstrated that doped graphene had superior current conductivity, higher electron mobility, higher thermal conductivity, and higher maximum temperature to those of reduced graphene oxide and silicon. Practicality wise, reduced graphene oxide is far easier to mass produce, as was found to still perform better than silicon-based transistors. Transistors form the backbone of the electronics industry; this study proves that a shift toward graphene-based transistors, doped ones especially, would make transistors stronger, more durable, and more efficient. With the already ubiquitous usage of transistors in our everyday lives, a switch to graphene could bring revolutionary benefits for electronics such as smarter cell phones, faster computers, and more accurate biosensors.
Effect of Weekly Time Spent Learning and Practicing Musical Instruments on the Intensity of Jitteriness among High School Teenagers in Mainland China
Authors: Yinfu Lyu
Date: 11-30-2023

Psychological disorders have become increasingly prevalent [Liang, 2021]. Among 875 cases, 33.03% of high school students in Mainland China possess some psychological problems [Liang, 2021]. Research has shown that successful musical engagement positively affects physical, social, educational, cognitive, and emotional health [Wang (2022).]. Unfortunately, Chinese teenagers aren't thoroughly studied the connection between musical training and mental health [Pang et al. (2017)]. Also, no statistically significant association between music playing and psychological well-being is seen in previous research [Wesseldijk et al. (2019)]. So, this study investigates how time spent practicing musical instruments influences the intensity of jitteriness among high school teenagers (15~18 years old) to provide insights about mental health-directed musical education in mainland China. A web-based, cross-sectional survey was conducted with a convenience and snowball sample of 200 teenagers from mainland China across 11 provinces. Models were conducted hierarchically using linear regression. In the univariate model, time spent practicing musical instruments weekly was associated negatively with the intensity of jitteriness (β=-0.151, p-value=0.033). In the multivariant model, participants' time spent practicing musical instruments per week was significantly and negatively associated with the intensity of jitteriness (β=-0.312, p-value=0.001), adjusted for confounding variables: total time spent on playing musical instruments, positive emotion intensity, negative emotion intensity (other than jittery), and age. The model was built using simple linear regression with r2=0.267. This study shows a significant negative association between weekly time spent practicing musical instruments and the intensity of negative emotions.
Beyond Language Barriers: Exploring the Effects of Implicit Message Decoding and Memory Retention
Authors: Insun Yoon
Date: 11-30-2023

This study examined the interplay between implicit message decoding, the time living abroad on language learning, and memory retention. The findings showed a link between the time spent studying abroad and the capacity to decode implicit messages. Additionally, having a narrator narrate educational materials visibly improved memory retention. The ability to decode implicit messages and memory retention were found to be correlated. These findings underscore the importance of interactive instruction and an immersive learning environment for language acquisition.
Potential for Conduit Hydropower at NYC's Newtown Creek Wastewater Treatment Plant
Authors: Reece Davidoff; Prathap Ramamurthy
Date: 11-30-2023

The Newtown Creek facility is New York City's largest wastewater treatment plant, with the capacity to treat up to 2.65 billion liters of combined sewage (rainfall and raw sewage) each day. After treatment at the plant, clean water flows out to the nearby East River via an outfall pipe. On average, the plant treats 810 million liters of combined sewage per day, which means there is potential to extract significant amounts of energy from the water flowing through the outfall. This paper first verifies that hydropower is a viable option at the plant by calculating the theoretical amount of power available from the running water, then determines what type of turbine would be the most appropriate for the outfall, and finally calculates how much electricity could realistically be generated by a turbine in the outfall. The bulb turbine was identified as the most appropriate type of turbine, and it was approximated that a bulb turbine in the outfall could produce, on average, 263 kilowatts of power. Considering that the plant runs year-round, a total of 2.24 gigawatt hours of clean electricity could be generated annually, which could be used to help power the energy-intensive wastewater treatment process or be fed back into New York City's electrical grid, where it could power up to 211 homes annually.&nbsp;
Treatment Optimization for Tumor Growth by Ordinary Differential Equations
Authors: Kenneth Chan; Chiu-Yen Kao, Jennifer Gordinier, Katherine Ganden
Date: 11-30-2023

Cancer is the second leading cause of death worldwide and with the disease having over 200 variations, it has not been cured yet despite being the priority of the medical field for decades. Due to the difficulty of human subject research, animal studies, e.g., mouse and Chinese hamster V79 tumors have been widely used to test the modeling of tumor growth due to their dynamic nature and ability to grow to high volumes within short periods of time. Mathematical models, including ordinary differential equations (ODEs), have been utilized to model tumor growth and study treatment of cancer. With most current models being selected only for mathematical convenience, recent studies have been focusing on determining the optimal treatment schedule for the most popular existing treatments of chemotherapy and radiation therapy. In this paper, three of the most established ODE models: the Gompertz, Von Bertalanffy, and logistic models are utilized to analyze which model most accurately fits existing tumor growth data for the Chinese Hamster V79 fibroblast tumor, various forms of immunodeficient mice tumors, and glioblastoma based on the minimization of the normalized mean squared error (NMSE). Next, the ODEs themselves were modified to simulate the growth of the tumors when exposed to treatment and determined which treatment schedule produced the lowest final volume of the tumors. The results of this research identify the optimal treatment schedules based on data from all three ODE models and also determine the ODE models that produce curves that most precisely fit the datasets.
Protein Stability in Pasteurized Cow and Goat Milk using Protein Gel Electrophoresis
Authors: Mai Nguyen; Haiyan Zhang, Barbara Schmidt
Date: 11-30-2023

Milk is a nutritious beverage that contains whey, casein, and milk fat globule membrane (MFGM) proteins. Pasteurization kills pathogenic microorganisms in milk, making it safe to drink. A high heat load during processing can affect the protein quality of milk. In this study, protein gel electrophoresis (Native and SDS-PAGE) was used to view the protein profiles of retail cow and goat milk samples pasteurized at different temperatures (145, 165, or 280 °F) compared to commercially spray dried milk (356–482 °F). SDS-PAGE provided better resolution of the milk proteins compared to Native-PAGE. There were clear species differences in the MFGM proteins. This could be used to spot adulteration of goat milk with cow milk, however none of the samples showed signs of adulteration. There were no overt patterns of protein degradation with increasing pasteurization temperature. However drying reduced band intensity, especially MFGM and whey proteins in the goat milk sample. Whereas most samples came from different herds where genetics and environmental conditions varied, two of the goat milk samples were from the same herd, one fresh and one spray dried. It was evident from these two samples that spray drying can alter proteins in goat milk. Even so, if fresh milk is unavailable or unaffordable, dry milk remains a valuable protein source.
Towards Sustainable AI: Mitigating Carbon Impact Through Compact Models
Authors: Eddie Zhang, Jixiu Chang, Ashley Yang
Date: 11-30-2023

As AI technology continues to advance rapidly, it is essential to address the environmental concerns associated with the increasing carbon emissions and their contribution to global warming. The expanding AI industry requires significant computing power, making it a potential major contributor to carbon emissions in the future. Unfortunately, our current understanding of AI models is very limited. We conducted a comprehensive analysis involving 12 distinct AI models, encompassing object detection, translation, and text-to-image generation tasks. Our findings revealed that smaller AI models can achieve equal or even better results compared to larger models while offering a significant reduction of carbon emissions. This highlights the potential for environmental savings by prioritizing smaller models. These findings underscore the importance of considering the environmental impact of AI models and encourage the adoption of strategies such as using smaller models and optimizing workload schedules to reduce carbon emissions. By prioritizing sustainability in AI development and deployment, we can work towards a greener and more sustainable future.
K-band Absolute Magnitude Relations of Red Clump Stars Separated by Age
Authors: Tiffany Zhang; Nicole Spinelli
Date: 11-30-2023

Red clump (RC) stars form a distinguishable clump on the color-magnitude diagram, making it a good distance indicator. Currently, the two main absolute magnitude estimation methods in the I-band (700–900 nm) conflict: one assumes a constant RC magnitude as supported by empirical data and one relates magnitude with other physical characteristics as supported by theoretical models. Studies attribute these discrepancies to population effects, such as dust, and recommend the K-band (2000–2400 nm) to minimize them. Past studies analyzed relations between the K-band magnitude, color, metallicity, and age, including those stratified by age at 2 Gyr. This study aims to investigate these different relations to clarify trends from past studies, discover new trends, and compare them to similar relations in the I-band. After analyzing cluster RC data from Gaia DR3, 2MASS, and Gaia-ESO DR5, K-band magnitude exhibits insignificant correlation in all relations (p&gt;0.05) but has the greatest dependence and the least root mean square error (RMS) with metallicity in the old RC. Significant correlation was found between I-band magnitude and metallicity for all RC and young RC (p&lt;0.05), especially the latter (R2 = 0.804). This consistency with theoretical trends suggests weaker I-band population effects than previously believed. Overall, these three relations yield more accurate predictions than the mean magnitude. Thus, studies cannot solely rely on the mean K-band or I-band magnitude to estimate distance, and magnitude relations stratified by age can potentially lead to more accurate RC distance estimations and a more accurately calibrated distance ladder.
Towards Generalizable Crop-Agnostic Plant Disease Recognition: An Unsupervised-Learning Approach
Authors: Dongmin Choi; Blessina Devakirubai
Date: 11-30-2023

In recent years, machine learning techniques have been incorporated in the agricultural industry to automate plant disease recognition. However, most existing frameworks are constrained to the recognition of certain species’ diseases, such as those of tomatoes, due to the severe imbalance in the published dataset. These methods tend to exhibit a strong bias towards specific plants, which require extensive retraining if the model were to accurately classify the disease of other plants. To ensure the system functions in a more realistic context, where images of various plants would be given, it is crucial to develop a generalized system that can recognize a diverse set of plants. To address the aforementioned problem, in this research paper, I propose a novel crop-agnostic plant detection framework. The method leverages contrastive learning, a type of unsupervised learning, to extract discriminative features from plant images regardless of their species. Moreover, the generalizable solution is capable of successfully distinguishing diseases from a dataset with imbalanced class and category. The method achieved an accuracy of 85.88% in the plant village dataset. Through experimental results, it has been demonstrated that the proposed method outperforms existing state-of-the-art methods by a significant margin.&nbsp;