ikatkov/journal-of-student-research-hs-articles huggingface dataset:
The Impact of Election Laws on the Turnout of Young People  An analysis of the relationship between election laws and youth turnout across the United States in the 2020 election
Authors: Alyvia Bailey
Date: 08-31-2023

The twenty sixth amendment of the United States Constitution says that no citizen over the age of 18 years old shall be denied the right to vote. Despite this constitutional amendment, there are many barriers that hinder this act of voting. Multiple types of barriers exist: location, time, money, and most important to this research, legislative barriers. Based on the voter turnout of all Americans across the country, it appears that these barriers seem to disproportionately affect young people and minorities. The low turnout rates among youth and minorities are a serious threat to the effectiveness of our representative democracy in the United States. Since the raw data for the turnout of minorities does not exist, the effects laws have on their turnout will not be determined in this paper. Minorities, however, will still be discussed in the review of literature because of their importance in regards to voter turnout.
Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes
Authors: Jia Lakhamraju; Mare Stewart
Date: 08-31-2023

        This paper explores the relationship between neural activity and behavioral performance in the form of visual working memory (VWM) task outcomes, by answering the question: Are there any significant differences in the firing rates of individual neurons during the distractor presentation period of a VWM task between success and error trials that can predict the outcome of a trial? Distractor-specific single neuron firing rates during a VWM task were analyzed to answer this question. A logistic regression was used to identify the predictive capability of neural firing rate on trial outcome with the neural activity of 51 cells from the lateral prefrontal cortex (LPFC) of a primate. This study found that a best-fit logistic model could predict the behavioral performance of the primate (success or error of the VWM task) with 63.01% accuracy, with additional machine learning techniques producing scores upwards of 68% accuracy. Moreover, greater firing rates in response to the distractor, indicating less efficient distractor suppression, accompanied the error trials of the VWM task. This suggests that stronger neural responses to task-specific distractors can hinder the attentional filtering required for efficient working memory, supporting previous research that found that distractor suppression is a mechanism that heavily influences WM efficiency. These findings indicate that people, particularly children, with disorders that affect WM capacity such as ADHD may experience stronger neural responses to distractors, and therefore inefficient distractor suppression, at the single neuron level when engaging in goal-oriented behaviors, which can significantly impact learning and other developmental processes.
Media Coverage of Macular Degeneration in Countries of Different Developmental Stages
Authors: Miriam Chang
Date: 08-31-2023

Macular Degeneration (AMD) is a chronic degenerative eye disorder that may not be well reported to the public. Current literature regarding media coverage of eye conditions tends to examine general visual impairment (ex. Tillery, 2017) instead of specific disorders such as AMD. The current research aims to examine how news media cover and frame information regarding AMD. Therefore, the researcher conducted an inductive content analysis on newspaper articles with the keyword AMD in the most circulated newspaper in three countries: The United States, Singapore, and Bangladesh. The three countries were chosen based on their varying socioeconomic status as the are categorized as developed, developing, and underdeveloped countries respectively. Results showed that the developed country reported more on treatments while the developing and developed countries reported mostly on the prevention of AMD. Writing strategies examined from all three news sites indicate episodic framing as the most prominent among all news sites and there is no difference in that aspect. Results are further analyzed, discussed, and implications were made as well.
A Continuous Oral-fluid Monitoring of Glucose (O.M.G.) Device with Near-field & Bluetooth Communication Capability
Authors: George Cheng
Date: 08-31-2023

      Around 34 million Americans have Type 2 Diabetes (T2D), while 88 million adults have prediabetes. Unlike the traditional invasive needle method, our proposed salivary glucose-monitoring device, OMG, can facilitate self-monitoring through a noninvasive method via Bluetooth modulation using cost-effective material (Nafion, Polydimethylsiloxane [PDMS], Bluetooth chips). The electrodes designed were coated with Glucose Oxidase (GOx), where a biological redox reaction occurs after being in contact with salivary glucose. When the interdigitated electrode (IDE) connects to the Bluetooth circuit, the impedance changes and modulates the electromagnetic reflection from the course, reflecting it as the “change of resistance” (which is proportional to the glucose concentration). Afterward, commercial chemical methods, ex-vivo, and in-vivo styles were employed to assess viability and usability. Data were assessed by creating several different comparisons between OMG and existing alternatives. Scanning Electron Microscope (SEM) images captured OMG GOx’s morphology, vector multimeters were used to collect data, and Glucose Assay Kits (Colorimetric) were used for OMG comparative analysis. NOREC (software) transfers measured fluctuations into graphical and numerical data. Testing results suggest that the trendline is reliable: R2 = 0.9292 for colorimetric and R2 = 0.9673 for our Performance Tests (ex-vivo and in-vivo), which was better than the gold standard: R2 = 0.8823. Further, we conducted multiple resistance tests, in which resistance and voltage significance was averaged to be p &lt; 0.001 and p &lt; 0.01, respectively. The non-invasiveness and portability demonstrate the necessity of developing such applications and novel, cost-effective smartwatch-based alternatives.
A Case Study on the Efficacy and Usage of Assistive Technologies in Howard County Schools, MD
Authors: Daniel Bi; Callie Casper
Date: 08-31-2023

Assistive technology is equipment that is designed to improve the capabilities of students with disabilities. For many students with disabilities, these technologies enable essential life skills such as daily communication and independence inside and outside of the classroom. This study assesses the efficacy, usage, and implementation of assistive technology in Howard County Public Schools (HCPSS) in Maryland, in order to determine any potential barriers and deficiencies towards the effective implementation of the said devices. A voluntary sample of 59 members of 1200 HCPSS staff was asked questions about the assistive technologies used in the county, their effectiveness, potential barriers towards implementation, training given, and available resources. Responses generally indicated that assistive technologies were beneficial, with the 83% of respondents selecting 4 or 5 out of 5 for the effectiveness of assistive technologies in academic and social contexts. Respondents elaborated that technologies allowed students with severe disabilities to communicate, socialize, work independently, and improve reading and writing skills. On the other hand, drawbacks such as distractions and dependencies created by the technologies were noted. Respondents felt confident towards the resources and support provided by the county, noting an assistive technology department and specialists available for support. However, respondents commonly reported that they were under-trained, received only occasional, limited workshop sessions, and were constantly behind on training. Thus, recommendations were drafted to improve training and staff awareness towards assistive technologies, such as required comprehensive training sessions for special education staff and a universal assistive technology guide to be provided.&nbsp;
Montessori Education: A Study on the Impact of Montessori Preschools in Washington on Short-Term Emotional Development in Children
Authors: Casey Tebben
Date: 08-31-2023

This research paper investigates how Montessori preschool education impacts short-term emotional development in children ages 3-5 in Washington state, compared to conventional forms of education. This study employs a multimethod approach including a quantitative parental survey measuring common adolescent behaviors on a five-point Likert scale along with a qualitative Montessori educator survey coded for key themes of Montessori education that support emotional development. The findings from the quantitative parental survey show that while both Montessori and conventional preschool students are on track for emotional development, there is a significant disparity as Montessori students were found to have higher emotional development. Furthermore, the educator survey suggests that there are four main aspects of Montessori curriculum: respect, independence/individualization, community/diversity, and conflict resolution. Overall, the results of this study provide valuable insights on the effectiveness of Montessori education in regard to promoting emotional development in preschoolers and how it could be further utilized to inform educational practices and curriculum in conventional preschools.
Women's Labor Participation in Ghana and Effects on Human Development: A Focus on Entrepreneurship
Authors: Audrey Wang; Antonio Bojanic, Bridget Hamill
Date: 08-31-2023

The purpose of this paper is to examine the gender-rooted challenges that women entrepreneurs face in Ghana and to analyze the impacts that increasing women’s rights and entrepreneurship have had on the country’s economy. Several indicators (including labor force participation, women's seats in national parliaments, the Women Business and the Law index, primary completion rate, and the gender inequality index) of gender equality and the prevalence of women’s entrepreneurship are compared with economic and human development trends. Through statistical regressions, the paper’s findings show a positive correlation between greater income equality and women’s seats in national parliament, as well as with the human development index, and conclude that the best way to reduce gender inequality is to increase the number of women in positions of power, as well as improve the education and skill development for women.
Effect of Brainwave Entrainment Using Binaural Beat Stimulation on Short-Term Memory
Authors: Meta VanGilder; Maren LaLiberty, Mindy Ray
Date: 08-31-2023

Auditory brain entrainment is a response to a rhythmic stimulus that increases the amount of a single brainwave frequency. It has been proposed to act as a key mechanism to heighten sensory intake.&nbsp; Auditory brain entrainment can be induced by listening to binaural beat stimulation. It has been shown that there is a positive effect on memory caused by using binaural beat stimulation. There has been research done on memory in young adults using binaural beat stimulation, but little in adolescents. Due to the lack of research on the effects of brain entrainment on memory in adolescents, this study attempts to show that a teen's working memory will improve after listening to a 40 Hz binaural beat for five minutes as compared to 0 Hz and 4 Hz.&nbsp; Participants were randomly assigned into three groups (0Hz, 4Hz, 40Hz) prior to the experiment. Participants completed the Sternberg Short-term memory test.The participants then listened to a binaural beat of their designated group for 5 minutes, after which they completed the second trial of the Sternberg Short-term memory test. The average change of speed recall and errors from no binaural beat to after the binaural beat stimulus was analyzed using a T-test of independent means. The results do not support the hypothesis that listening to binaural beat stimulation of 40Hz would improve short-term memory compared to 4Hz and 0Hz.
Detecting Emotions in Audio Data of Patients with Post Traumatic Stress Disorder using Convolutional Neural Networks
Authors: Rohan Gupta
Date: 08-31-2023

As humans, we have an effortless ability and a high accuracy to identify another human's emotions through the tone, pitch and pace of their speech, even the emphasis and stress placed on each word. However, people with a traumatic experience suffering from Post Traumatic Stress Disorder (PTSD), 24.4 million people in the USA, can often repress emotions&nbsp; making it difficult for therapists to identify their patients genuine emotions and to treat them appropriately. Using an upcoming field of emotion detection in Artificial Intelligence (A.I.), I identified the human emotions from speech. Instead of using an audio transcription based model, I opted for a newer image based model, RESNET-18, which is widely used and utilizes spectrograms to preserve the subtleties in speech, critical in distinguishing emotions. To train the model, I used the RAVEDESS dataset which consists of wav files with eight different emotions. I was able to achieve an overall accuracy of 82% (greater than human detection by 25%). Specifically, I achieved 99% for no stress class (happiness), 97% for neutral class, (neutral, calm, and surprised), and 8\5% for stressed class (fearful, sadness, anger, disgust). I also found that the model got an accuracy of 87% when only trained on males, with continued training an overall accuracy above 90% is definitely achievable. In conclusion, it is possible to find the emotions of PTSD patients, and in the future, continued research can help improve the lives of people who are not able to express their true emotions.
Roscovitine’s Effect on D. melanogaster with TDP-43 Nuclear Loss Amyotrophic Lateral Sclerosis
Authors: Mira Ramachandran, Satvika Aruva; Jessica Eliason
Date: 08-31-2023

Amyotrophic lateral sclerosis (ALS) is a neurological disease that leads to motor neuron death, causing muscle atrophy and paralysis. The majority of ALS patients die from respiratory failure within 2–5 years. By 2040, the incidence of ALS is predicted to increase worldwide by 70%. ALS has no cure. TDP-43 protein dysfunction is present in ~97% of ALS patients. Past ALS research focused on TDP-43 aggregation in the cytoplasm of neuronal cells; however, loss of TDP-43 from the nucleus is now considered the main contributor to neurodegeneration. Drosophila larvae with dTDP-43 nuclear loss exhibit locomotion deficits and reduced levels of cacophony, a neuronal calcium channel required for neurotransmitter release. When cacophony was restored in dTDP-43 nuclear loss larvae, locomotion was rescued. Roscovitine is a drug that increases calcium influx in neuronal calcium channels, essentially performing the same function as increased cacophony. The purpose and novelty of this research are to determine if a roscovitine supplement can improve the locomotion of a TDP-43 nuclear loss ALS model of Drosophila melanogaster. The larval locomotion assay was used to validate the ALS symptom of muscle weakness. The movements of larvae on an agar plate were recorded. Using ImageJ, the displacements and speeds of the larvae were determined. Results indicate that ALS larvae fed roscovitine performed significantly better on the locomotion assay than ALS larvae fed normal food (p-value &lt; 0.0001). This research provides insight into the role of neuronal calcium channels in TDP-43 nuclear loss and calcium channel agonists’ potential in treating ALS.
The Limits of AI Content Detectors
Authors: Hongyu Wu; Tom Flanagan
Date: 08-31-2023

As ChatGPT became a popular and powerful language model used by people worldwide in 2023, the problem of students using it to cheat on schoolwork became palpable. While many existing AI content detectors can detect AI-generated texts, such as GPT-2 Content Detector and GPTZero, the accuracy of an AI content detector in detecting&nbsp;generated essays that have been post-edited by humans is unknown. This research discovered the limitations of the GPT-2 Content Detector and answered the question, “How does human post-editing of AI-generated high school English essays affect the result of an AI content detector?” Ten English essays were generated using ChatGPT Plus based on prompts from high school English teachers. Each essay was then edited in 5 different ways to create pairs of unedited and edited essays. All unedited and edited essays were evaluated using GPT-2 Output Detector Demo, and then the results from the detector were studied and analyzed. It was found that introducing spelling mistakes in generated essays and processing the essays with QuillBot will make the result of AI content detectors less accurate. The findings from this research can be used as a guide for companies developing AI-generated text detectors, making them more accurate when dealing with edited generated text. The findings can also be helpful for schools and educators, because knowing that students can edit essays to bypass AI content detectors, educators can develop new ways to examine students’ writing ability.
Impact of Gender Bias in Training Data for Machine Learning Models predicting Myocardial Infarction
Authors: Victoria Harding Bradley
Date: 08-31-2023

The use of biomarkers reference ranges derived from clinical trials to detect and diagnose the presence of cardiovascular disease (CVD) and the occurrence of myocardial infarction (MI) is well established.&nbsp; However, the predominance of older male participants in these trials has been shown to contribute to increased rate of misdiagnoses among women.&nbsp; The application of Machine Learning (ML) to medical diagnosis promises the potential to improve accuracy.&nbsp; However, ML also has the potential to perpetuate this problem of gender bias in trial data leading to worse outcomes for female patients.&nbsp; This research found that models trained using data containing only male patient data were less accurate at predicting MI among female patients than those trained using data sets with greater representation of females.&nbsp; For the model trained only with male patient data, this increased false negative rate corresponds to 2% of female patients with MI not being correctly diagnosed.&nbsp;&nbsp; Addressing this issue will require (a) the collection of more female patient data to support the construction of training data sets which accurately reflect the patient population (b) consistent reporting of gender mix and other demographic information such as age when ML model performance is reported.&nbsp;
IQ as an Indicator of Academic Stress, Study Habits, and Academic Success
Authors: John Shadwell; Justin Matyas, Grace Escamilla
Date: 08-31-2023

Intelligence quotient (IQ) and its impacts have been studied heavily in previous research. However, studies have not yet sufficiently applied IQ to an academic environment, especially in high-school aged students. To fill this gap in the field of study, this study utilized a mixture of qualitative and quantitative methods to measure trends in the behavior of students at different IQ levels and evaluate the strengths and weaknesses of student populations. The study’s participants, comprising of 21 high schoolers, voiced and quantified their opinions on their personal experiences regarding stress, study habits, and academic success. Ultimately, the trends in high IQ and low IQ groups suggest that IQ is a valuable statistic in educational environments which can be used by educators to improve student weaknesses. The exposure of IQ scores in academic settings would provide crucial insight into the behavioral trends of student groups and can make a lasting positive change in education.
Interleukin-6 Levels in Nasal Secretion as a Potential Diagnostic Tool for Alzheimer’s Disease  Exploring the Feasibility and Clinical Significance of IL-6 Measurements in Nasal Secretions
Authors: Lailla Burka; Jessica Lancaster
Date: 08-31-2023

Alzheimer's disease (AD) is a debilitating condition affecting millions worldwide, and early detection is crucial for effective treatment and management. Due to the cluster of amyloid beta plaques and neurofibrillary tangles, the neurons in the brain begin to undergo gradual and irreversible neuronal loss. This is why early detection of AD is crucial for effectively treating and managing the disease. However, the current diagnostic methods, such as imaging scans, are not always accessible and affordable and cannot be diagnosed early on. This study investigated the potential of nasal secretion as a non-invasive diagnostic tool for AD. The study employed two assays, Bicinchoninic acid (BCA) and Enzyme-Linked Immunosorbent Assay (ELISA), to measure protein and Interleukin-6 (IL-6) levels in nasal secretion samples. Blood samples were also collected to serve as a comparison tool. The findings suggest that nasal secretion may be a promising diagnostic tool for AD, with elevated levels of IL-6 found in the nasal secretion of mice with AD-like pathology. The total interleukin-6 concentration in Alzheimer’s disease mice was between 0.1367 pg/mL and 0.14233 pg/mL, compared to the nasal secretion in the healthy mice cohort between 0.094 pg/mL and 0.11 pg/mL. The study contributes to the research on the IL-6 biomarker in AD. It shows that utilizing nasal secretion as a diagnostic tool could allow for early detection and improved quality of life for patients. Future research should investigate the accuracy of nasal secretion as a diagnostic tool and compare it to other current methods.
Rice Fields as Mosquito Density Indicators for Malaria Prediction in Nigeria: An Anthropological View
Authors: Gabrielle Wong; Heidi Cooke
Date: 08-31-2023

The World Health Organization (WHO) identifies high-risk areas for disease outbreaks in African countries (WHO, 2019a). Sub-Saharan Africa, including Kebbi state in Nigeria, is particularly susceptible to vector-borne diseases (WHO, 2019a). Malaria is a significant health concern in Kebbi state, where rice fields serve as breeding sites for mosquitoes (CDC, 2020). This paper proposes an early warning system that utilizes satellite imagery and household data to predict mosquito populations in Kebbi State. Integrating machine learning and satellite imagery can inform disease control strategies and public health interventions. Additionally, this approach presents opportunities for anthropological research into the political aspects of malaria and rice farming in Kebbi state, Nigeria. Anthropologists can analyse the socio-political and economic factors influencing malaria persistence while considering the perspectives of local communities. Understanding the intricate relationships between farmers, mosquitoes, and public health officials can contribute to the development of more effective and equitable malaria control strategies. The poverty rate of approximately 72.0% in Kebbi state further complicates healthcare accessibility (Nigeria Health Watch, 2021). By focusing on this region and employing a multidisciplinary approach, this paper aims to address the urgent health challenges and support sustainable solutions for malaria prevention in Kebbi state, Nigeria.