bluuebunny/crossref_metadata_2025 huggingface dataset:
The Texas Tower Shooter: An Analysis of Charles Whitman
Authors: Sajeev Ghazal
Published: August 2024

This application paper will examine common attributes of serial killers, such as biological impairments in their brains and other aggravating factors: experiencing child abuse, having violent fantasies, and fitting into the categorization of organized/disorganized killing. These factors will be analyzed to determine how accurately they apply to serial killer Charles Whitman and to what extent they influenced his decision to commit a mass shooting. Information from empirical studies on serial killers, review articles on neurological defects concerning violence, and book chapters were assessed to establish what aspects relate to Charles Whitman. The results indicate that Charles Whitman had common traits of a serial killer, such as undergoing an abusive childhood, being classified as an organized killer, and having violent thoughts. The research supports the fact that Whitman had a neurological defect, a tumor on his amygdala that could have contributed to his actions. The overall findings suggest that further neurological research on how biological elements impact violent actions can contribute to a better understanding of preventive measures regarding violence in serial killers.
What Makes Music, Music? A Look At Congenital Amusia Expression In Tonal Language Speakers
Authors: Wang Jocelyn
Published: August 2024

Research advancements in recent decades have given rise to the notion that there exist specialized brain structures to facilitate music perception independently of other mental processes. The study of pitch-related congenital amusia, a disorder wherein an individual has a reduced ability to perceive and distinguish pitch, has allowed significant contribution to the development of this music-specific brain model. This paper aimed to find how, if at all, congenital amusia expression is affected by the speaking of a tonal language – a language that relies on pitch as one distinguishing factor for lexical meaning – as well as implications for the musicspecific brain model that arise from said effect. A literary review was conducted, with a focus on literature published in the past decade. It was conversely found that congenital amusia affects tonal language processing, with the difficulties in musical pitch discrimination extending to language-related pitch discrimination. This finding provides further insight for the workings of the music-specific brain in that the overlaps between musical and lexical pitch could suggest either that pitch is fundamentally not a solely musical characteristic, or that tonal language has an inherent musical aspect. Future research on this subject should seek to clarify this understanding. Additional research in neighbouring fields, including cultural and emotional psychology, linguistics, and music therapy, is also discussed.
Financial Performance of Tech Companies in the Pandemic and Post-Pandemic Eras
Authors: Lee Seongwon
Published: August 2024

The existence of COVID-19 was revealed at the end of December 2019 and was declared a global health emergency in March 2020. Due to its strong ability to disseminate, the pandemic spread all over the world. Because of COVID-19, the basic lifestyles of people changed: online stores, online entertainment, and other online services flourished. During the pandemic, seven tech companies, namely Apple, Amazon, Netflix, Google, Facebook, Tesla, and Microsoft, were able to adapt to the new global trend; therefore, the seven companies grew rapidly. This research paper focuses on analyzing two factors: the performances of the seven companies during the pandemic and after the pandemic, and the future challenges that the companies must face. To assess the performances of the companies during and after the pandemic, Yahoo Finance and Profitspi were used to collect the price/earnings (P/E) ratio, daily stock price, market capitalization, and revenue of the seven companies from 2019 to 2022. The data revealed a general trend in the stock prices, in which the prices of the stocks increased from 2019 to late 2021 or early 2022. Unfortunately, the prices of the stocks declined after 2022. These findings show that the performances of the companies were constantly rising until the peak of COVID-19, and started to decline after the peak of the pandemic. These results also indicate that the strong performance of tech companies during the pandemic may not be sustainable after the pandemic, particularly given the challenges of increased competition and more stringent regulations.
Entangling Alliances: America, Japan, and China in the Wake of WWI
Authors: Bian Yunlin
Published: August 2024

In 1914-1917, the United States actively encouraged China to join World War I (19141918), but Japan firmly resisted China’s participation in the war. At the end of the war, the attitudes of the United States and Japan switched. Japan tried to encourage China to join the war, whilst the United States tried to prevent China from participating. The change in the attitudes of the United States and Japan towards China is by no means a coincidence, but a concrete manifestation of the competition between the two sides for the dominance of their diplomacy with China. This essay discusses such a switch in their attitude with primary sources and the reason behind such switch. The first stage, starts from 1914 and ends in early 1917, is Japanese deterrence and American encouragement; the second stage, starts from early 1917, is Japanese encouragement and American deterrence.
New Home Court: How Political Relationships and Economic Motivations Shaped the Experiences of Chinese NBA Players in the United States in the Early 21st Century
Authors: Guo Angel
Published: August 2024

Like many other American businesses, The National Basketball Association (NBA) has gradually capitalized on China’s enormous market potential since the establishment of the Open Door Policy. At the turn of the century, the NBA exploded in international popularity with the introduction of three native Chinese basketball players onto its rosters. This paper delves deeper into the dynamics between the NBA and the Chinese trio, Wang, Yao, and Yi, as not only foreign athletes, but as reflections of US-China relations. Through a chronological and correlational exploration of this relationship, the paper argues that while the NBA grew economically by expanding into Chinese markets, Chinese athletes in the NBA faced both political barriers exiting China and increased cultural pressures once in the United States. The experiences of these athletes serve as a microcosm of the intricate interplay between economics, politics, and culture in the context of international relations. This research concludes that the Chinese athletes were used both as economic pawns by the NBA and diplomatic pawns by China, highlighting the relevance of sports as a lens through which to navigate and understand the complexities of global affairs.
Construction of the X^2 Goodness-of-Fit Test
Authors: Ma Michael
Published: August 2024

The goal of statistical tests is to use data to learn about the nature of the system from which it is collected. It is an important bridge between raw data and interpreting its meaning/ drawing conclusions from it. One recent use of statistical tests is in COVID-19 tests, where statistics is used to determine the false positive rate of such tests to assess their accuracy.

In this paper we will focus on one of the most used statistical tests, the Chi-square test, f irst published by Karl Pearson in 1900 to investigate the ‘deviations from the probable’. We will first discuss the general procedure of hypothesis testing, followed by the construction of an appropriate estimator for statistical models before finally, finding the limiting distribution of the estimator. Using these properties, we will construct the Chi-square distribution and discuss its relevance to hypothesis testing. Python simulations using the Chi-square test will be generated to investigate the effect of multiple variables on its statistical power.
The Perception of the Accessibility of the United States Healthcare System for Asian Immigrant Women
Authors: Zhan Madison
Published: August 2024

Asian immigrant women living in the United States (U.S.) are more likely to encounter barriers when accessing the U.S. healthcare system. These challenges can include language differences between physicians and patients, cultural incompetency within the healthcare system, and discrimination. Additional qualitative research is needed to better understand the unique perspectives and needs of Asian immigrant women living in the U.S. The objective of this research is to study the U.S. healthcare system from the perception of Asian immigrant women and explore potential challenges and experiences with discrimination. The methodology for this study was a qualitative interview design using a thematic analysis approach. A convenience sample of n=7 Asian immigrant female study participants was recruited. The themes from the qualitative analysis included cultural differences, cultural barriers, language, healthcare accessibility, and potential improvements to the U.S. Healthcare System. More research should be done regarding the extent to which immigrants actively seek out and utilize healthcare services dedicated to educating and assisting immigrant populations. Further research is needed to understand how non-English speakers can be better informed about the healthcare options and translation services available.
Mass-to-Luminosity Ratio and Its Correlation To Dark Matter Distributions Across Different Galaxy Types
Authors: Ding Jaymin , Phaneesh Pradyota , Atluri Abhiram
Published: August 2024

Dark matter is believed to play a pivotal role in the structure and dynamics of galaxies. Unlike ordinary matter, dark matter does not emit light or energy or interact with electromagnetic forces. This study explores dark matter distribution in galaxies and compares various galaxy types. We utilize the Spitzer Photometry & Accurate Rotation Curves database, focusing on latetype and early-type galaxies, including Blue Compact Dwarf, Lenticular, Small Magellanic, Tight Spirals, Loose Spirals, Irregular Spirals, and Elliptical galaxies. Using centripetal acceleration and Newton’s Law of Universal Gravitation, we derive a galaxy-mass equation within a certain radius. Luminosity within a given radius is graphed through conversions from the database. Results indicate that the mass-to-luminosity ratio generally increases with radius across all galaxy types, suggesting an intertwined relationship between mass, luminosity, and dark matter distribution. The increasing mass-to-luminosity ratio with radius implies that all galaxies tend to have more mass relative to their luminosity as we move further from the center. This supports the idea that dark matter becomes more prominent in the outer areas of the galaxy, forming a dark matter halo and exerting gravitational forces on visible matter to influence the structure of the galaxy. Our results imply that dark matter is a significant part of galaxies.
An Analysis of the Relationship between Color and Morphology of Galaxies with Redshift
Authors: Yu Andrew
Published: August 2024

The research topic derives from my deep curiosity in galaxy evolution. I am asking if there is a correlation between the distance to a galaxy (measured by its redshift) and its color, measured by u-v (ultraviolet magnitude subtracted by visible light magnitude) color index, for galaxies of various morphological types (spirals, ellipticals or irregular shapes). Astronomical data such as redshifts, morphological types, and color indices were queried from the database SIMBAD. Python was used to perform data analysis and to graph data. The best fit function was deployed for polynomial regression with different polynomial degrees to determine the best fitting trend. One-directional trending was not seen throughout, rather, a complex pattern roughly represented by y = -0.027x^4 + 0.150x^3 + 0.053x^2 - 0.453x + 1.192 was identified. The downward trend was present only in the redshift ranges z < 1 and z > 3. The study showed that for each morphological type, there is not a simple correlation between a galaxy’s distance and its color index. However, a cross-morphological type comparison indicated irregular shaped galaxies are bluer than spirals, and spirals are bluer than ellipticals, which was the case throughout the entire redshift range.
The Involuntary Nature of Beliefs
Authors: Jiang Caroline
Published: August 2024

Beliefs are fundamental to an individual’s behavior and identity, yet they cannot be picked and chosen by an individual. This paper reviews the role of the individual’s voluntary choice and control in the formation of beliefs. It is concluded that the formation of beliefs, consisting of five main stages (Connors & Halligan 2017), is involuntary. Cognitive biases and heuristics and external influences are innate features of the human mind and the nature of beliefs, rendering extensive control over beliefs impossible. Moreover, conscious awareness of involuntary influences is not thorough and does not induce will or control over changing and interfering with the process of belief formation. Though the limitations of the mind and the inability for control seems unfavorable and undesirable, developing an open and adaptive mindset proves to be even more significant. Acknowledging the involuntary nature of beliefs cultivates a perspective that allows for effective examination upon the execution and application of beliefs.
Using a Systems Biology Approach To Reveal a Common Molecular Signature Across Neurodegenerative Diseases
Authors: Fareed Kareem
Published: August 2024

Becoming increasingly prevalent in recent years, the three major neurodegenerative diseases - Alzheimer’s (AD) Parkinson’s (PD), and Huntington’s (HD) - continue to avoid most attempts at treatment and early diagnosis. Operating under the same pathogenic umbrella of neuronal death, it stands to reason that these, and all other neurodegenerative diseases would share risk factors by virtue of their similar pathologies. The aim of this study is to analyze and compare the molecular signatures of the three neurodegenerative diseases, using a holistic approach, in order to uncover a common signature among them. To achieve this, three gene expression datasets are used, one for each disease. A molecular signature of each of the three neurodegenerative diseases is defined using system biology approaches. The resulting signatures are interrogated for any statistically significant overlap between the diseases at different genomic levels. The results of the overlap analysis point to common malfunctions in various autophagic, apoptotic, and mitophagic pathways. Locating a shared signature of the three diseases opens new avenues for drug repositioning across neurodegenerative diseases, offering novel ways of prevention and treatment and supporting future research toward precision medicine.
The Effectiveness of Loss Framing and Fear Appeal in Motivating Climate Action
Authors: Xu Minna
Published: August 2024

Current climate change communication involves various forms of framing: outcome framing, attribute framing, and point-of-reference framing. The focus of this paper will be on loss framing, a type of outcome framing. Past research on loss framing, including fear appeals, generally agrees that loss framing leads to suppression and denial of climate change information. This study aimed to determine whether fear appeal, a type of loss framing, effectively motivates people indirectly affected by climate change to take action. This review paper analyzed the results of 11 papers found on August 26, 2023, on ScienceDirect, with the keywords “climate change,” “fear appeal,” and “media” filtered for research articles. The results indicated that the papers reviewed generally found that either loss framing is effective on its own, loss framing is effective with specific solutions, that the effectiveness of loss framing may vary, or that motivational framing is more effective. This paper concluded that, generally, loss frames are more effective than motivational frames, but the effectiveness depends on the specific situation and group. There is a gap in the research concerning the effectiveness of loss framing climate change media, specifically on people indirectly experiencing climate change.
Determining the Most Accurate Text Classifier Model for Predicting Whether Online Product Reviews are Human or Computer Generated
Authors: Collomb Leonard
Published: August 2024

In recent years, fake online product reviews have become an increasingly prevalent issue for e-commerce platforms (McCluskey, 2022). This project attempts to address this issue of pervasive fake reviews negatively impacting e-commerce platforms by focusing on the automated detection of computer-generated reviews. The study evaluates several text classifier algorithms, including Logistic Regression, Support Vector, Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Extra Trees, AdaBoost, Bagging, and Gradient Boosting, and compares their performances in distinguishing computer-generated reviews from genuine reviews. These classifiers are assessed based on their accuracy and F1 scores (the harmonic mean of their precision and recall scores) in distinguishing between human and computer-generated reviews after being trained on a dataset comprising of approximately 20,000 computer-generated and approximately 20,000 human-written reviews, which have been transformed into numerical vectors using TF-IDF vectorization. Results indicate that Logistic Regression consistently outperforms other classifiers, demonstrating robust accuracy and F1 scores across trials. K-Nearest Neighbors classifier shows the poorest performance, likely due to challenges in highdimensional text data. Ensemble methods, such as Random Forest and Extra Trees, deliver notable success, leveraging multiple decision trees to enhance predictive performance. AdaBoost and Gradient Boosting also demonstrate competitive results, showcasing the capabilities of adaptive boosting. The study concludes that Logistic Regression is the most accurate classifier for detecting computer-generated reviews, offering insights into its simplicity and effectiveness in capturing linear patterns within the data. Further exploration could involve tuning hyperparameters for models with poor performance, and exploration with even more models.
The Evolution of the Skirt, its Alternatives and their Meaning in the Modern Era
Authors: Hwang Thea
Published: August 2024

This article depicts the skirt as it morphs from a full-length, heavily petticoated Victorian silhouette to the mini and its trousered alternative from mid-19th century bloomers to the various pant styles worn by women today. Alongside these evolutions of women’s dress, the larger socio-historical context in the United States and the women’s movement as a whole are noted, in particular the continual cycle of developments in fashion adapting women’s wardrobes to accommodate their changing role and place in society, which in turn feed further refinements in the design and function of womenswear. Despite the social progress in the U.S. from the 1850s to the 2020s, it would appear that an individual may dress exclusively to reflect their personal choice and identity only to the extent of any pre-existing privilege they enjoy. For women and girls, even with feminist movement in its fourth wave, recent litigation over school dress codes indicate that pants remain a gendered issue. And with gender increasingly understood as a complex rather than a binary identity and expression, the skirt returns to the conversation over social norms, this time for men and people read as men who may elect to wear the occasional skirt.
Enhancing Child Safety in Vehicles: Advanced Solutions for Mitigating Injury and Saving Lives
Authors: Li Jonathan Z.
Published: August 2024

Despite significant advancements in vehicle safety technology, motor vehicle crashes continue to pose a significant risk to child passengers. This paper addresses the persisting issue of inadequate child safety measures in vehicles by proposing innovative solutions to mitigate injuries and save lives. Drawing upon original research and a comprehensive review of existing literature and safety data, this study explores the implementation of adaptive airbag systems tailored to the unique needs of child passengers.

The research methodology involves developing a video-based camera that accurately identifies infants, toddlers, and children within vehicles. Experiments and testing regimens determine optimal airbag deployment angles and forces based on passenger height and weight. Additionally, the paper discusses the integration of a system to adjust airbag deployment angles in real time, ensuring maximum protection for child occupants.

Consumer insights are essential, prompting a survey to gauge public opinion on current child safety measures and proposed solutions. The survey results highlight widespread concern among respondents regarding the effectiveness of existing safety precautions, particularly airbag systems.

Ultimately, this paper presents a holistic approach to enhancing child safety in vehicles, combining original research efforts with insights from existing literature to propose practical solutions. By implementing adaptive airbag systems tailored to the unique needs of child passengers, this study aims to significantly reduce the incidence of injuries and fatalities in motor vehicle crashes, safeguarding the well-being of young passengers for generations to come.
Embrace Emptiness to Prevent Suffering: A Journey through Santideva’s Philosophy on Perception, Reality, and the Path to Liberation
Authors: Liao Richard
Published: August 2024

The paper explores the profound philosophy of Santideva, focusing on the concept of emptiness and its implications on human agency and ways to prevent suffering through an argument reconstruction using analogies. A major issue Santideva highlights is how conventional truth, shaped by societal consensus and labels, veils the ultimate truth – the reality of things. Emptiness, as the absence of intrinsic nature in phenomena, becomes a cornerstone for understanding reality beyond conventional appearances. Santideva’s arguments challenge the notion of a self and illustrate how conditions influence experiences and the illusion of a continuous, unchanging “I .” This examination extends to self-awareness, refuting the mind’s ability to perceive itself independently. Navigating through the complexities of suffering and categorizing it into various forms - all stemmed from attachments and ignorance – that ultimately advocate the path to liberation through the Four Noble Truths, this paper encourages a practical application of Santideva’s philosophy in daily life, highlighting the need for practice and action over mere contemplation, paving the way toward understanding and embracing the elusive quest for liberation from suffering. The paper does not provide an answer; nonetheless, it should help inform readers without previous knowledge and researchers of the questions Santideva implores us to consider and offer a modern interpretation of Santideva’s definition of how to attain liberation from suffering.
Deriving and Verifying a Community Vitality Index through a Machine Learning Model Based on House Price Data
Authors: Li Mark
Published: August 2024

Recognizing the complexity and limitations of the approach to produce a traditional Multi-dimensional Community Index (MD-CVI) as well as high cost and time-intensive, the study explores another approach that establishes a machine-learning model to derive a community index by leveraging contemporary house prices. This method has several significant advantages over traditional methods, providing a more efficient and reliable way to assess community well-being. The model produces a dynamic Community Vitality Index (CVI) that quickly captures changes within a community and enhances the MD-CVI’s sophistication by selecting and combining any geographic regions generated by clustering algorithms, employing machine learning model and house sold price reduces human errors from questionnaires, and potentially improves accuracy. The study also exemplified and verified the model via house data from Minnesota, Texas, and Connecticut, spanning the last five years. Subsequently, comparing the house-price-based CVI with traditional MD-CVIs reveals a consistent alignment in their community index rankings. The conclusion is that the house-price-based CVI, as second research, is a viable proxy and a benchmark for a traditional MD-CVI, indicating an innovative approach for the stakeholders, including the government, education institutions, health and welfare organizations, etc. in assessing community well-being and making informed policy decisions regarding the flow of public funding of community-based projects. This research has the potential to significantly impact policy decisions, ensuring that public funding is directed to the communities that need it most.
Ideology - Partisanship Alignment and Quality of Democracy: A Correlational Study
Authors: Zhu Hengrui
Published: August 2024

This paper identifies whether a correlation exists between the extent to which a democracy is programmatic and the quality of its democracy. In order to achieve the objective, the research will:

1. Develop an indicator for the Programmatic Score of democracies. 2. Assess if there exists a significant correlational relationship between the programmatic score of countries and their respective quality of democracy. This paper devises the Programmatic Score of democracies as an indicator of the extent to which voters vote based on their ideology, instead of other factors such as socio- economic and racial status.

To achieve objective 1, the paper uses Wave 7 of the World Value Survey to determine the proportion of the voting population that votes for the party and the ideological composition of the voters of the party. To achieve objective 2, the paper determines the quality of democracy to be Freedom House’s Freedom Score of the country, with countries rated 70 or above characterized as democracies.
Donde Comen Dos, Comen Tres: Investment Discrimination Against Latino-Owned Businesses
Authors: Catalano Thomas
Published: August 2024

The paper addresses the systemic underinvestment of Latino-owned businesses in the United States. The research examines the disparity in access to capital for Latino-owned businesses, the mechanisms through which this disparity is rooted and persists, and the impacts that this early-stage disparity plays on the future success of businesses. The work employs comparative data between Latino-owned businesses and white-owned businesses to empirically establish the investment disparity.
The State of Financial Literacy and Financial Education in the United States
Authors: Zhou Jeff
Published: August 2024

The world we live in is more complex than ever. We can now transfer money through mobile payment apps such as Venmo, budget through online services such as Mint, and invest in the stock market through free-trading apps like Robinhood. As a result, teenagers and young adults can easily spend and accumulate debt without ever seeing paper money change hands. Without these physical barriers to spending and the vast subscription services and online shopping opportunities, people are more susceptible to losing control of their personal finances. Due to new financial technology and globalization, personal finance has become increasingly complicated in the United States and throughout the world. This paper reviews the literature on the current state of financial literacy in the United States, including the efforts to improve financial education. This paper will also discuss how financial literacy is measured in the existing literature and consider whether financial education improves adult financial literacy or personal finance outcomes. Finally, this paper will outline a few proposals for future research. learning.
OCD and its Effects on Memory
Authors: Li Mina
Published: August 2024

As rates of mental health disorders increase throughout the years, more and more studies have emerged to investigate their effects, such as the effects of Obsessive-Compulsive disorder on memory. Obsessive-Compulsive Disorder was first described in the Diagnostic and Statistical Manual of Mental Disorders in the 1980s.1 It is organized into 5 main categories: checking, contamination, symmetry/ordering, intrusive thoughts, and hoarding and can be measured using the Y-BOCS. OCD is caused by genetic, neurobiological, behavioral, and environmental factors. Neuroimaging studies reveal overactivation of the orbitofrontal cortex and the basal ganglia. Scientists have discovered the effects of OCD on episodic and procedural memory. In 2004, Robert M Roth conducted a Pursuit Rotor Task with 46 participants. The results reveal that the OCD group has enhanced procedural memory, likely due to the overactivation of some parts of the brain. Furthermore, in 2011, Mika Konishi used the “directed forgetting paradigm” method on 45 participants. The results reveal reduced retrieval inhibition and difficulties in selective encoding in OCD brains, which lead to decreased memory recall performance.
Sports and Venture Capital Firms
Authors: Han Annie R.
Published: August 2024

Venture Capital (VC) firms in sports have varied targets, timings, and budgets according to their firm types and they usually establish management teams to execute due diligence. This paper mainly focuses on the interaction between VC Firms and sports-related companies by introducing the background and differences among various VC firms in sports. A detailed analysis of a sports tech investing VC firm will be displayed and the execution of due diligence will be explained. Moreover, this paper will also illustrate the development of VC firms in the sports tech sector over time and evaluate their prospects.
Venture Capitalism in the Mental Healthcare Sector after the COVID-19 Pandemic
Authors: Wang Eddie
Published: August 2024

The COVID-19 pandemic was to date the largest-scale disease outbreak of the 21st century. It has had far-reaching consequences in all aspects of life. One of the most significant is the global increase in the prevalence of mental health concerns, in turn placing mental healthcare companies front and center for demand. Investors, deprived of many industries which did not escape the financial crisis brought on by the pandemic, may view the emerging mental health sector as an auspicious target. This paper analyzes the impact of COVID-19 on venture capitalism in the mental healthcare sector through comparisons of pre- and post-pandemic states of the financial market and use of case studies of various companies involved in mental health. A generally positive correlation can be seen between the outbreak of the virus and growth in investment. Using these findings, this paper suggests directions for venture capital investment, such as in startups, which balance out potential profit and risk.
A Detailed Review of Factors that Influence Early Brain Development
Authors: Patel Kishan
Published: August 2024

This paper aims to explore the different factors that affect brain development. Early neurodevelopment hinges on several brain processes. Neurons and supporting glial cells have to form inside the neural tube and migrate after doing so. An insulative layer forms around the midsection of the neuron as a part of its maturation and further development. From matured neurons, neural networks are formed which store information that is first learned, then repeatedly retrieved. However, overproduction leads to cell death, which keeps networks functioning efficiently. External influences can be manipulated to help the efficacy of brain development. The factors that influence neurodevelopment on the neurological scale and that are crucial in paving the way for ideal brain maturation will be analyzed.
An Essay on Refugees
Authors: Zhang Yueer (Ariel)
Published: August 2024

This essay explores some of the issues that refugees face, citing examples across a range of national contexts. The author highlights important aspects of refugee crises and glosses some of the key concepts associated with these issues and events. The essay presents a case for greater attention to this important matter.
The Relationship between Color and Morphology of Galaxies with Redshift
Authors: Yu Andrew
Published: August 2024

The research topic derives from the author’s deep curiosity in galaxy evolution. He is asking if there is a correlation between the distance to a galaxy (measured by its redshift) and its color, measured by u-v (ultraviolet magnitude subtracted by visible light magnitude) color index, for galaxies of various morphological types (spirals, ellipticals or irregular shapes). Astronomical data such as redshifts, morphological types, and color indices were queried from the database SIMBAD and calculated. Python was used to perform data analysis and to graph data. The best fit function was deployed for polynomial regression with different polynomial degrees to determine the best fitting trend. One-directional trending was not seen throughout, rather, a complex pattern roughly represented by y =-0.027x4 + 0.150x3 + 0.053x2- 0.453x + 1.192 was identified. The downward trend originally hypothesized was present only in the redshift ranges z < 1 and z > 3. The study showed that for each morphological type, there is not a simple correlation between a galaxy’s distance and its color index. However, a cross-morphological type comparison indicated irregular shaped galaxies are bluer than spirals, and spirals are bluer than ellipticals, which was the case throughout the entire redshift range.
Utilizing Type II Restriction Endonucleases (EcoR1 and BamH1) in Complex with gRNA for Precise Gene Editing
Authors: Platzman Matthew
Published: August 2024

CRISPR-Cas9 is one of the leading and most commonly employed gene editing processes. This technique uses guide RNA (gRNA) to recognize and excise specific DNA sequences. However, this process frequently elicits off-target effects within the genome. In consequence, the future of CRISPR-Cas9 as a therapeutic remains uncertain. This review aims to provide a new perspective on an alternative to the specificity, efficiency, and safety of genome editing by using type II restriction endonucleases, EcoR1 and BamH1, to recognize a specific DNA sequence (the recognition site) and cleave the targeted DNA adjacent to or within the recognition site. This article discusses the potential of utilizing intrinsic properties of these restriction endonucleases and repurposing them to prevent off-target mutations that may result in cancers. Preliminary research exhibits the specificity of using restriction endonucleases, however, it remains understudied in the field of cancer therapeutics. This review focuses on the potential to employ EcoR1 and BamH1 to prevent DNA sequence mutations during gene editing.
Analyzing the Failures of the Self-Strengthening Movement
Authors: Cao Jason
Published: August 2024

When analyzing the late Qing Dynasty, the Self-Strengthening Movement is often regarded as a significant attempt at modernizing China’s military, education, and industry. Initiated by reform-minded officials like Li Hongzhang, the movement sought to reinvigorate China by applying Western knowledge and technology to traditional Chinese institutions. However, while the Self-Strengthening Movement did establish a comparatively modern military, institutions for Western studies, and a nascent industry, it lacked the depth necessary to bring substantive change to the nation. Indeed, the Qing Dynasty would continue to suffer military defeats and political turmoil that led to its eventual collapse. Through investigating the limitations of the Self-Strengthening Movement from three directions- military, education, and industry- this paper aims to highlight the structural issues that plagued modernizing initiatives. The paper attributes the eventual failure of the movement to the lack of systemic changes to China’s political institutions. The need to adopt not only Western technology but also its democratic institutions remains applicable to China today.
Do the Results of Elections Express the Will of the People?
Authors: Zhu Yeqingqing
Published: August 2024

Elections are essential to democracy, enabling citizens to choose representatives and ensure accountability. The 2020 US presidential election’s reflection of the people’s will is examined, considering factors like the Electoral College, polarization, misinformation, and representation challenges. While the election was legally valid, there were representation gaps. Reforms and dialogue are needed to address these issues and strengthen democracy. The election’s legitimacy is supported by majority preference. Biden’s win with 52% of the popular vote and 306 electoral votes displayed broad support. Voter turnout and diversity further validate the process. Critics claim media bias, misinformation, and money influenced voters, but these arguments lack credible evidence and ignore voter agency. Democracy encourages varied opinions, transparency, and accountability. Diverse representation in government is key for legitimacy. The 117th Congress is historically diverse, reflecting America’s variety in race, gender, religion, and ideology. While critics point to underrepresentation and polarization, these challenges can be addressed through inclusivity and dialogue. The election’s legitimacy is affirmed by its conduct and accountability. High turnout, transparency, and international observation validate the process. Elected officials fulfilling campaign promises and engaging with diverse perspectives enhance democracy’s vitality. The 2020 US election expressed the people’s will through majority preference, diverse representation, and democratic legitimacy. Reforms should address gaps, misinformation, and polarization. Enhancing the election process, civic education, and inclusivity will fortify democracy.
Using AI to Improve Breast Cancer Diagnosis and Its Effects on Health Care Cost
Authors: Zhang Jeffrey
Published: August 2024

AI and Machine learning have been taking over the modern world. Machine learning is a branch of artificial intelligence (AI) which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. It is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. This study evaluated machine learning and its applications in healthcare, especially in the diagnosis testing process. Decision Tree model, Random Forest model and Support Vector Classifier model were tested and compared. This study demonstrated that machine learning models can be used to optimize the diagnosis testing process. The new and improved breast cancer test is shown to be as accurate if not more than before. Test efficiency was improved by 45%. The application of machine learning should be encouraged, and such practice will lead to significant cost saving.
How Angel Investors Identify Startups with Potential for Success
Authors: Jamieson George
Published: August 2024

This paper serves as a reference to prospective angel investors looking to find a way to get started in investing in their first start-ups. Identifying start-ups with greater potential for growth than others is the focus and identifying factors that allow companies to grow and survive are articulated in this paper. Since a large percentage of companies fail within the first several years, being able to select companies with greater chances than others to succeed is crucial to potential investors.
Are There New Types of Compact Stars?
Authors: Zhou Yihong
Published: August 2024

This research examines two hypotheses about the inner structure of neutron stars: the hybrid star theory, which presents neutron stars with different layers, and the strange star theory, which presents neutron stars as an almost homogeneous sphere. Each reveals the possible existence of a new type of compact star. Moreover, possible future discovery of sub millisecond pulsars is vital to justify the strange star hypothesis.
Large Language Processing Model ChatGPT and its Impact
Authors: He Qiaochu (Echo)
Published: August 2024

This research paper explores the transformative impact of ChatGPT, a Large Language Processing Model developed by OpenAI, on various facets of contemporary society. Through an extensive examination of academic literature and empirical research, the study delves into the capabilities, applications, limitations, and ethical considerations surrounding ChatGPT in the realms of education, healthcare, and business.

In the current world, Artificial Intelligence is playing an increasingly important role in different aspects of human society due to its rapid advancements. One of the classifications for AI is the Large Language Processing Models (LLMs), which are trained to have a massive dataset of text for dialogue with users through chatbots. ChatGPT, developed by the OpenAI company, has demonstrated impressive capabilities as an LLM. Through an analysis of data and research conducted about ChatGPT, we can see that the development of ChatGPT by OpenAI has already created significant impact and controversy in different aspects of human society. This is important because AI is likely to be the most influential technology of our time and will fundamentally change people’s way of life. This paper aims to systematically review literature related to the impacts, and controversies, and discuss areas of future research.
Is Safety More of a Priority Than Fun?
Authors: Dong Peichen (Payson)
Published: August 2024

The human desire for security has deep evolutionary roots, prioritizing safety over enjoyment. Early humans faced various threats and developed vigilance to survive. This tendency to prioritize security is evident in the negativity bias, where negative stimuli receive more attention than positive ones. Understanding this desire for safety helps us appreciate its importance in modern enjoyment. Safety is paramount when seeking pleasure. Neglecting safety protocols can turn enjoyable experiences into tragedies, as seen in drowning statistics. Ensuring safety in recreational activities enhances enjoyment and minimizes risks. Fear, stemming from the absence of security, hinders enjoyment. Crime rates and neighborhood safety affect individuals’ well-being. Addressing safety concerns fosters relaxation and enjoyable experiences. Prioritizing safety fosters trust and security in communities, improving mental health and overall satisfaction. Safe environments enable social connections, fulfilling higher-level needs. Balancing thrill-seeking with safety is essential. While thrill-seekers report higher happiness levels, they engage in riskier behaviors. Finding a balance between excitement and safety is crucial.
Dealing with invisible scars of war: PTSD in Veterans
Authors: Dimitrova Alexandra
Published: August 2024

The long-term effects of war are extremely present in modern society. However, mental injuries such as posttraumatic stress disorder are commonly overlooked, as they are more obscure than physical wounds and casualties. Today, PTSD continues to affect veterans, and many treatments that have been developed so far are still lacking in effectiveness or have not been thoroughly tested. Prevention treatments can target groups that match characteristics such as deployment details and demographics of those who are most likely to develop PTSD. These can include both demographics and details about deployment. HRVB and CBM-I have been able to decrease PTSD scores in certain populations. Social support, specifically from civilians, is also crucial in protecting veterans from developing PTSD or worsening the severity of PTSD symptoms. Various first-line treatments such as evidence-based psychotherapies and pharmacotherapies show significant lowering in symptoms as well as PTSD remission rates, but these treatments are still ineffective for many patients, whether this is because of personal preference or a general lack of efficacy of therapies. The use of psychedelics and psychedelic–assisted psychotherapy, alcohol interventions, breathing-based meditative sessions, and potentially 3-MDR have shown a decrease in PTSD symptoms for those who preferred not to use first-line treatments or found them to be ineffective. More research should be done to find and assess the current alternative treatments. They have generally proven to be less effective than other, common evidence-based treatments, but are still possible replacements for traditional treatments for some veterans with PTSD.
Implying community vitality index through housing factors
Authors: Li Mark
Published: August 2024

The Community Vitality Index (CVI) is pivotal in assessing community well-being, informing decision-making processes, encouraging community participation, and efficiently distributing resources to enhance residents’ overall quality of life. Leveraging contemporary housing data and sophisticated multivariate regression algorithms allows us to train a machine-learning model for a real-time vitality index. This enables prompt recognition and response to evolving community dynamics, all without delays.
Heart Attack Prediction Using Machine Learning
Authors: Miner Natalie
Published: August 2024

Heart disease is one of the leading causes of mortality due to heart attacks and other complications. When a person has a heart attack, their heart muscle begins to die because of a lack of blood flow due to a blocked or reduced blood supply. The most severe effects of a heart attack can be reduced with prompt identification and treatment. Machine learning can help medical professionals make an earlier diagnosis of heart disease and start treatment, reducing complications and saving lives. Using various attributes relevant to detecting heart disease, a machine-learning model was created that predicts a person’s risk of heart disease. The logistic regression model created in the study has an overall accuracy of 85%. Ultimately, this model could play an important role in predicting heart disease and preventing the severe effects of a heart attack.
Music therapy and Postnatal Depression: a Literature Review
Authors: Wang Xinyang (Francesca)
Published: August 2024

Postnatal depression is a type of depression occurring after a woman has given birth. Music therapy, which means using music to improve health, is considered an alternative treatment for postnatal depression. This literature review aims to investigate whether and to what extent music therapy has an effect on reducing postnatal depression symptoms. While some results show that music therapy is potentially beneficial, the research done on this topic is very limited and future studies should be done to further explore the impact of music therapy on postnatal depression.
Prediction of Heart Failure Using Random Forest and XGBoost
Authors: Gao Aidan
Published: August 2024

Heart Failure (HF), a type of Cardiovascular Disease (CVD), is a prevalent illness that can lead to hazardous situations. Each year, approximately 17.9 million patients globally die of this disease. It is challenging for heart specialists and surgeons to predict heart failure accurately and on time. Fortunately, there are classification and prediction models available that can assist the medical field in efficiently using medical data. The objective of this study is to enhance the accuracy of heart failure prediction by prediction modeling a Kaggle dataset composed of five sets of data over 11 patient attributes. Multiple machine learning approaches were utilized to understand the data and forecast the likelihood of heart failure in a medical database. The results and comparisons show a definite increase in the accuracy score of predicting heart failure. Integrating this model into medical systems would prove beneficial for aiding doctors predictions of heart disease in patients.