ikatkov/journal-of-student-research-hs-articles huggingface dataset:
Cytogenetics and Molecular Diagnosis in Horse Infertility
Authors: Jiwon Choi; Mayra Cerna, Terje Raupsepp
Date: 11-30-2023

As the popularity of horse breeding stays steady, many breeders and owners send their horses samples to cytogenetics analysis for signs of infertility or other genetic abnormalities. Here, we report three cases of horses that were subjected to chromosome analysis due to infertility issues. Unfortunately, in the case of the H304 mare, the horse has a normal 64, XX female karyotype and no chromosomal abnormality was observed to explain the infertile issue. In the case of the H305 mare, the horse has a 64, XY male karyotype which indicates genetically male and no sign of chromosomal abnormality under cytogenetic analysis. However, PCR analysis indicates the loss of the SRY in the Y chromosome. This is a typical male to female sex reversal and one of the most common genetic sex abnormalities in horses. In the H306 mare, the horse has only one X chromosome; 63, XO, called X monosomy. This is the most common sex chromosomal abnormality in horses. The combination of clinical cytogenetic analysis and the use of PCR are the strongest tool to determine the chromosomal abnormality in the equine industry and the case H305 is a good example to support how these two different approaches compensate each other to generate a reliable diagnosis. Determination of genetic abnormalities using two techniques will help horse breeders and equine practitioners to make the most informed decisions about breeding plans.  &nbsp;
Period poverty’s impact on the futures of its most marginalized groups  How the upward mobility of women and girls in the US is influenced by period poverty.
Authors: Karuna Damle; Jaz Riley
Date: 11-30-2023

  The main objective in this paper was to conceptualize how period poverty impacts the upward mobility of women and girls in the United States. With the topic of period poverty gaining awareness and the idea of menstruation becoming more mainstream, the impacts of menstruation on a menstruator’s future must be acknowledged also. One of the main ways to identify these impacts, specifically from a financial perspective, is through socioeconomic growth as it demonstrates an intersection between a person’s economic stability and the opportunities they have been given from a younger age. In this paper, period poverty and its influence on upward mobility is broken into demographic groups of girls in middle and high school, women in college, low-income women, and incarcerated women. This research connects the areas of period poverty and upward mobility to identify how financial precarity and menstruation limit the futures of its most struggling groups.
Machine Learning for Risk Prediction of Cardiovascular Disease: Current Advances and Future Prospects
Authors: Abhaya Saridena, Ananya Saridena; Jothsna Kethar
Date: 11-30-2023

One of the main causes of death worldwide is cardiovascular disease (CVD). Effective treatment of this global concern depends on early detection, as well as management. Currently, people with established heart issues are treated by physicians and other medical experts, with detection at later stages of CVD. However, the burden of cardiovascular disease treatment can be significantly reduced if it was possible to accurately estimate a patient's CVD risk at the initial stages. Machine learning techniques have emerged as a viable method for improving CVD risk prediction, which enables treatments to be more effectively tailored to each individual patient's needs. An in-depth analysis of current research on machine learning applications for CVD risk prediction is provided in this publication. The paper discusses the benefits of applying machine learning techniques, various prediction algorithms, performance assessment, and current research limits. Our findings suggest that machine learning methods are useful for predicting CVD risk and have the potential to improve clinical judgment, which may help to lessen the burden of cardiovascular disease in the future. This research also helps to shape the medical field by providing insights on treating similar deadly diseases using AI and machine learning models.
Improving Deforestation Detection Accuracy in Noisy Satellite Images with Contrastive Learning-based Approach
Authors: Jitae Kim, Lim Lee, Sihu Park; Lenny Musungu
Date: 11-30-2023

Deforestation, the large-scale destruction of trees, has far-reaching biological and environmental consequences that pose a significant threat to the environment. Accurate deforestation detection is crucial for successful conservation initiatives and effective land management. Over the last decade, numerous deforestation detection methods utilizing spaceborne photography have been proposed. However, these methods tend to be sensitive to unique image noise in the satellite domain by virtue of the diverse aerial characteristics and air qualities in different regions. To solve this problem, we propose a novel noise-robust deforestation detection framework with a contrastive-learning based approach. The proposed framework consists of two phases: contrastive learning, which aims to extract similar feature embeddings for the same category, proceeded with transfer learning in order to develop the deforestation classifier. Remarkably, the proposed contrastive learning approach successfully handles noisy input satellite images during the feature extraction process. Upon conducting validation, we have found that the proposed method outperforms existing deforestation detection methods by a significant performance gap, highlighting the effectiveness of the proposed contrastive learning approach.
Deep-Learning Based Automatic Ergonomic Assessment Using Webcam Data
Authors: Owen Lu; Clark Hochgraf
Date: 11-30-2023

Primarily due to increasing computer use, people are spending more and more time sitting in front of a desk every day. However, prolonged sitting has been associated with tiredness, hypertension, and pain in areas like the lower back or shoulders. These symptoms arise for a variety of reasons, but musculoskeletal disorders in particular are largely associated with poor postures. The adverse results caused by poor postures can be controlled with proper training and monitoring. This study attempts to provide automatic ergonomic assessment using only webcam data. Since laptops, desktops, and phones are now widely available and equipped with built-in cameras, this solution is accessible and convenient for most people. More importantly, automatic posture assessment may help to prevent conditions associated with poor posture by giving reminders whenever improper posture occurs. To create our model, we make use of Mediapipe, which provides a solution to identifying keypoint locations from an image. By training our MLP classifier on this key-point data, we achieved a 96.96% test F1 score, indicating that our system serves as a convenient way to assess posture while maintaining high performance. To illustrate our results, we perform a final video classification by overlaying the model’s pre-dictions on each frame.
Image Quality Enhancement via Machine Learning: A Unified Approach to Super-Resolution, Denoising, and Low-Density Enhancement
Authors: Woochan Jung; John Blofeld-Watson
Date: 11-30-2023

Problem: The escalating demand for high-quality images across various applications has underscored the necessity for advanced image enhancement techniques. Traditionally, denoising, super-resolution, and low-density enhancement, the three key image enhancement techniques, have been approached independently, resulting in separate developments for each. Unfortunately, a unified framework that seamlessly combines all three techniques and surpasses individual method performance has been lacking. Proposed Idea: The objective of this research is to develop a unified image enhancement framework that not only unites these techniques but also substantiates its superiority over existing individual methods through an extensive series of experiments. The proposed method utilizes cascade autoencoder architectures to generate high-quality enhanced images. In addition, an auxiliary artifact type prediction module has been introduced to enhance the noise-awareness, resulting in improved accuracy. Result: The proposed method demonstrates superior performance in achieving state-of-the-art accuracy when evaluated against three image quality metrics on various public datasets. Additionally, the practical application of the proposed method showcases its efficacy in effectively solving real-world problems.
A Method for Training Object Scale Estimation System using Feature Extraction Enhancement with Depth Estimation
Authors: Kyungryun Kim
Date: 11-30-2023

In recent years, machine learning-based object scale estimation has been growing in popularity, as the significance of the technology lies in its potential for use in many industry fields. Although several methods have been proposed, the possible applications of this technique are limited due to its insufficient accuracy. Hence, a human-level accurate system is needed for the technology to be applied in the real-world domain. This research paper proposes a novel object scale estimation system that incorporates the feature extractor, disentangled feature maps, depth estimator, object localizer, and ground truth depth map. The input of the proposed system is an image, which is inputted into the feature extractor to create disentangled feature maps. These feature maps are then extracted by the depth estimator to generate a depth map, and by the object localizer to create a predicted bounding box around the object. The trained feature extractor can extract disentangled size-related features from the inputted image by jointly training the depth estimator and object localizer. The use of disentangled features boosts the performance of the proposed system. In addition, we propose an actual scale converter module to calculate the actual size of the inputted object. Throughout the experiments, the proposed method has proven that it is superior compared to other state-of-the-art methods. The proposed method achieves an IoU (Intersection over Union) value of 0.8113 on the COCO dataset.  &nbsp;
Kavain Hydroxylation in Kava Metabolism: Computational Analysis
Authors: Youlin Feng
Date: 11-30-2023

Kava, a non-alcoholic beverage derived from the Piper methysticum plant, commonly also known as Kava, has a prominent role in the cultures of Oceania due to its calming and soothing effects. These psychoactive properties have sparked interest beyond Oceania and in the scientific community, who see potential for medicinal applications. However, alongside these beneficial effects, consumption of Kava has been linked to potential hepatotoxicity, raising safety concerns. Understanding the cause of this hepatotoxicity requires a detailed exploration of Kava's metabolic processes, specifically those involving kavain, its primary constituent. Recent studies have found the hydroxylation of kavain to 12-hydroxykavain as a key step in Kava's metabolic pathway. Yet, the precise mechanism of this conversion and the hydroxylation intermediates involved remain poorly understood. To address this knowledge gap, our study employed Spartan, a computational chemistry software, to model the hydroxylation process. We analyzed the stability and chemical properties of the proposed reaction intermediates, providing a detailed view of this critical phase of metabolism. Our analysis reveals that three of the five proposed intermediates appear stable, suggesting they may play significant roles in the hydroxylation process. This finding also enabled us to narrow down the possible reaction pathways of hydroxylation. Having a better understanding of the key step of Kava metabolism could guide the development of safer, more effective Kava-based products, balancing the therapeutic potential of Kava with a reduced risk profile.&nbsp;
Impact of Non-Thermal Particle Acceleration on Radiative Signatures of AGN Jet-Cloud Interactions
Authors: Krish Jhurani; Moninder Modgil
Date: 11-30-2023

This study investigates the complex dynamics of AGN (Active Galactic Nucleus) jet-cloud interactions, particularly focusing on the impact of non-thermal particle acceleration on the resulting radiative signatures. We utilize advanced computational simulations, tracking changes in jet properties and emissions over a span of 0.2 Myr (millions of years). The research design incorporates the modeling of jet-cloud interactions, with a key focus on variations in the jet's density, velocity, and magnetic field. Findings reveal a two-fold increase in the magnetic field strength up to ~5 μG due to cloud incorporation, which, coupled with an elevated non-thermal particle population, enhances synchrotron emissions, shifting the spectral index from 2.2 to 2.4. Inverse Compton scattering saw a 30% increase within the first 0.125 Myr, reflecting in an abrupt X-ray and gamma-ray emissions spike. Furthermore, the jet's light curve flux variability in the X-ray band showcased an initial peak increase of about 28% by 0.175 Myr, settling to a 20% increase by 0.2 Myr, attributable to cloud disruption and absorption. Conclusions drawn from these findings confirm our hypothesis that non-thermal particle acceleration dramatically influences the radiative signatures of AGN jet-cloud interactions .It underscores the necessity of considering such acceleration processes in modeling AGN jet-cloud interactions and posits that these changes could be instrumental as observational indicators, thereby contributing to more accurate interpretations of AGN activity and evolution.  &nbsp;
The Role of Government in Combating Child Labor: India as a Case Study
Authors: Aaliya Gupta
Date: 11-30-2023

The issue of child labour has been a persistent thorn in India’s growth story. Despite the existence of several national and international level legislations pertaining to this issue, completely eradicating the practice of employing child labour has been a difficult and arduous task. This policy paper attempts to explore the reasons behind the persistence of this issue, analyse the adverse impacts on children engulfed in this practice and further suggest some policy-based solutions to this issue by looking at the impact of landmark court cases.
The Relationship Between College Experiences and Middle-Aged Entrepreneurs’ Success
Authors: Alexander Slater; Soo Park
Date: 08-31-2023

This study aims to explore the relationship between success in the world of entrepreneurship and the college experience. Colleges have been growing in cost in recent years and it becomes even more imperative to explore the value of a college education when pursuing a career in entrepreneurship. This study collected data using semi-structured interviews with questions revolving around various characteristics learned during college. The data was analyzed with a thematic analysis that was meant to identify the overarching characteristics that entrepreneurs felt were the most important factors for their success. The study concluded that the most important factors for success as an entrepreneur were interpersonal skills, diversification of perspectives, and critical thinking skills. These results demonstrate that college is valuable for the development of well-rounded entrepreneurs and potentially informs future curricula.&nbsp;
The Impact of the 2009 US-backed Coup on the Political, Social, and Economic Spheres of Honduras
Authors: Nashla Turcios
Date: 08-31-2023

This qualitative study explores the perspectives of seven Honduran citizens regarding the 2009 coup in Honduras, with a focus on themes of political instability, economic struggles, social polarization, migration, and international intervention. The participants expressed a range of views on the coup and its aftermath, with some highlighting the negative impact of the coup on Honduran democracy and others suggesting that it represented a necessary corrective measure. The study also examines the role of the United States, in the coup and its aftermath, with a majority of participants expressing negative sentiments towards US intervention in Honduras.&nbsp;
Falling Out of Postural Instability: Evaluating the Contribution of Somatosensation to Standing Balance in Parkinson's Disease
Authors: Katherine Shi, Andrew Monaghan; Daniel Peterson
Date: 08-31-2023

People with PD (PwPD) experience a variety of motor symptoms, including postural instability, impaired gait, tremors, and falls. Falls among PwPD are especially debilitating, as they can result in fracture risk, increased progression of the disease, and even death. Previous research has demonstrated that the somatosensory system, consisting of tactile sensation (which is responsible for feelings of touch and pressure) and proprioceptive feedback (which is important in joint position sense), is crucial for reactive balance control. The primary goal of this project was to determine the unique contributions of the tactile and proprioceptive systems to standing balance control in PD. Participants at the Gait and Balance Disorders Lab at Arizona State University were asked to maintain their balance while experiencing 3 sensory vibration conditions from vibrotactile transducers 1) a control condition with no vibration, 2) vibration underneath the feet to disturb tactile sensation, and 3) vibration on top of their feet (dorsiflexor tendon) to manipulate proprioception. Another goal was to identify predictors of somatosensory impairment during standing balance (i.e., participants affected most by sensory vibration) by associating clinical characteristics with change scores in balance outcomes from the no vibration-control condition to the tactile and proprioceptive stimulation conditions. The results of this study indicate that vibrotactile stimulation had minimal impacts on standing balance responses. This study also found few predictors of somatosensory impairments. Further research is needed to enhance clinical efficiency in designing treatments that have the most impact on improving balance control and preventing falls.
Quantifying the Dynamics of Data Augmentation Hyperparameters for Facial Expression Recognition
Authors: Ryan Lin; Peter Washington
Date: 08-31-2023

Automated recognition of facial expressions is a central component of systems used in an expanding array of domains. For a computer to automatically recognize affect, copious amounts of data are required to successfully train the model. It can often take a lot of work to collect and label data. In recent years, researchers have applied numerous data augmentation strategies to increase the diversity of the data within training datasets. Here, I examined the most common data augmentation strategies to determine which strategies result in higher performance for the facial expression recognition machine learning model. I first tested each data augmentation technique by itself and compared their performances. I next ran an ablation study with the augmentation strategies. I then analyzed the effect of dataset size on the marginal contribution of data augmentation. I find that augmentation does not always improve performance. When the dataset size is small, it results in a degradation of model performance. The accuracy of models with data augmentation starts to outperform the models with no data augmentation when the training dataset size is greater than a certain threshold. These results highlight the importance of considering dataset size when applying data augmentation to computer vision.
Test Anxiety and Perfectionism
Authors: Melissa Landsman; Grace Escamilla, Justin Matyas
Date: 08-31-2023

Past research demonstrates that test anxiety is an important component impacting academic performance. Perfectionism has been investigated as a factor associated with test anxiety. The purpose of this study is to contribute to an identified research gap by examining the distribution and association of perfectionism and test anxiety among high school students. This study included 74 high school students with the majority consisting of juniors and seniors in a suburban high school in Northeastern United States. Using the Westside Test Anxiety Scale and Short Almost Perfect Scale, 41% self-reported high test anxiety and 66% self-reported perfectionism in the high school student sample. Correlational analyses revealed a strong positive association (r = .47) between perfectionism and test anxiety, a strong positive association (r = .49) between discrepancy perfectionism (i.e., self-criticism) and test anxiety, and a weak positive association (r = .18) between standards perfectionism (i.e., striving) and test anxiety. The findings are consistent with existing studies of other age samples and further the understanding of test anxiety and its association with perfectionism in high school students. Implications for school-based interventions to manage test anxiety and perfectionism and directions for future research are considered.  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Keywords: Test Anxiety, Perfectionism
An Analysis of Costs and Solutions to High Recidivism Rates in Florida
Authors: Sofia Krivitsky, Aarush Santoshi, Sophia Li, Matthew Diomidous; Krassimir Penev
Date: 08-31-2023

Recidivism affects a significant portion of convicted offenders. It represents the culmination of many factors like social isolation, a lack of work opportunities, and drug abuse. This project evaluates the risks and costs of recidivism in Florida's correctional facilities in terms of the physical cost of incarceration and the social cost that imprisonment has on communities. We derive and analyze data from six main sources: past recidivism trends from the Florida Department of Corrections, data of police employment, data of median income data, drug arrest data, and data of unemployment trends. We then evaluate the feasibility of measures involving drug rehabilitation, educational programs, police employment increases to discourage recidivism and facilitate reentry into society by using symbolic regression to calculate future trends. The R-squared values ranged from 65.4% to 97.3%. A primary component analysis (PCA) was performed with post hoc Kaiser–Meyer–Olkin, which yielded a value &lt; 0.6, and Bartlett’s Sphericity, which yielded a value &lt;&lt;&lt; 0.0001, tests, suggesting a substantial correlation. A Monte Carlo analysis was then performed to predict the total instances of recidivism through 2024. This research showed that increasing police efficiency and investing in drug rehabilitation services should be prioritized by the state of Florida.&nbsp;
The Coevolution of the Understanding and Treatment Modalities for Phantom Limb Pain
Authors: Rachel Rui Hong; Chloe Cavanaugh
Date: 08-31-2023

Phantom Limb Pain (PLP), an inappropriate sensation in a missing limb, is a post-amputation phenomenon that occurs in up to 80% of amputees. The specific sensations vary from person to person, but some common reported sensations include warmth or coldness, itching, tingling, and electric shock. Some patients also perceive a specific position or movement of the phantom limb. Most amputees experience PLP with varying degrees of intensity, frequency, and duration. About 5-10% of amputees continue to have severe PLP for many years after amputation. As technology and scientific understanding have evolved, phantom limb pain has become better understood over the centuries, from its first mention from Ambroise Pare in the 1600s to the usage of a variety of medication, medical therapy, and surgery options available today. Although the field of medicine does not yet have a curative treatment for PLP, treatment modalities have advanced from the use of wooden pegs to the latest and advanced prosthetics. By summarizing the growing understanding and evolution of treatment modalities for the medical condition from its first mention in the 16th century to the current 21st century, we can begin to appreciate the years of study and collaboration to current knowledge of phantom limb pain.