Sleep is an essential part of our life. Good sleep improves productivity, quality of life, and social outcomes like academic and work performance. However, the demands of modern lifestyles, including academics, sports, social activities, and physical changes during adolescence, can disrupt adolescent sleep patterns. This study aims to examine the relationship between sleep duration, sleep quality, and academic performance in students aged 14–18 years. Methodology: This study examined the sleep behaviors and academic performance of high school students aged 14–18 in the greater Boston area. A questionnaire assessed sleep duration, quality, time to fall asleep, daytime sleepiness, and the voluntary or involuntary nature of sleep deprivation. Participants also reported demographic details, including age, gender, and grade, and rated their school performance and its perceived connection to sleep. Data was analyzed using jamovi 2.3.28, with descriptive statistics summarizing variables as percentages. A Chi-square test was used to explore associations between sleep variables and academic performance. Results indicate that the study included 36 respondents, all of whom completed the survey. Participants comprised 53% male, 42% female, and 6% gender-neutral students. Sleep duration was reported as ≤7 hours (50%), 7–8 hours (33%), and greater than 8 hours (17%). Sleep duration was primarily influenced by mandatory schoolwork (44%) and discretionary activities (39%), with fewer citing health-related reasons or environmental factors (17%). In conclusion the study found no significant association between sleep quality, sleep duration, or daytime sleepiness and academic performance. Participants with poor sleep quality experienced more daytime sleepiness.

Affective Music Generation (AMG) is a type of music generation that incorporates specific emotions into musical compositions. The challenge of accurately integrating emotional expression into AMG remains underexplored, particularly in the evaluation of model performance across various architectures. Given the growing role of affective music in entertainment and therapy, this review’s objective was to evaluate current AI models to identify the most effective architectures for emotional accuracy. This study covered foundational AMG concepts and models such as Russell’s Circumplex Model of Emotion, long short-term memory (LSTM) models, and transformer models. The current state of affective music generation is reviewed by comparing machine learning model architectures and their emotional accuracy. After applying the inclusion and exclusion criteria, fifteen studies were selected through a systematic database search of Google Scholar and PubMed. The methodology followed PRISMA guidelines for a systematic review. Key findings through a narrative analysis of the studies showed that deep learning models, particularly transformers, are the most common approach for AMG due to their ability to handle sequential data efficiently. In addition, Russell’s circumplex model of emotion was often used for emotional representation and quantification.

In the past decade the world has witnessed many breakthroughs in AI, the purpose of this paper is to apply some of those breakthroughs to use in the medical field especially in imaging and see how well they perform. Brain tumor classification using convolutional neural networks (CNNs) can be computationally intensive and models may perform inconsistently. This paper addresses this inconsistency by utilizing multiplicative weight update method, an algorithm that dynamically adjusts the weight assigned to each model's predictions based on their performance, amplifying the influence of accurate models while diminishing the impact of less reliable ones, in combined CNN models to improve overall classification accuracy and mitigate the impact of poorly performing models within the combined model. In this paper deep-learning models were trained and used to classify three types of brain tumors; meningioma, glioma, and pituitary tumors. The combined model consists of three sub-models. Model 1 outperformed other models with the highest training and validation accuracy, achieving a combined model accuracy of 97.24%, closely matching the baseline accuracy of 97.95%. The multiplicative weight update method effectively reduced the influence of Model 3, which underperformed due to fewer convolutional filters, while enhancing the contribution of Model 1. Overall, this paper demonstrates how methodological innovations, such as the multiplicative weight update algorithm, can improve model reliability, offering a scalable solution for the application of machine learning in brain tumor classification.

Seawater desalination has emerged as a promising solution to meet the increasing demand for freshwater, with reverse osmosis (RO) being the predominant technology due to its energy efficiency. However, challenges such as membrane fouling and brine management persist. Concurrently, seawater harbors a wealth of valuable minerals, with magnesium ions (Mg2+) standing out for its abundance and industrial significance. This study explored the synergistic integration of seawater desalination and mineral extraction technologies to address the global challenges of water scarcity and resource depletion. Through a systematic analysis, this study identified magnesium ions as a prime target for extraction from seawater, considering its concentration and market value. Various extraction techniques, including precipitation, adsorption, nanofiltration, and electrodialysis, were evaluated for their efficiency, cost-effectiveness, and environmental impact. Integration of mineral extraction processes with RO desalination was proposed, either preceding or following freshwater production, to optimize resource utilization and minimize RO scaling challenges. Key considerations such as operating costs and environmental sustainability were emphasized, guiding the selection of extraction technologies. The manuscript underscored the importance of achieving high purity minerals and sustainable extraction practices to support long-term economic viability and environmental responsibility. This manuscript provided a comprehensive framework for the simultaneous production of freshwater and valuable minerals from seawater, while identifying critical areas requiring further investigation.

Coastal urban areas have been facing instability from sea level rise, natural disasters, and extreme heat that will significantly escalate if not mitigated properly. To minimize the socioeconomic impacts of climate change, coastal cities must create effective adaptation frameworks. Numerous case studies have been performed on existing adaptation programs, but they have focused on coastal urban areas clustered in an area – limiting their ability to draw generalizable conclusions. To fill this gap, this study compared the threats Los Angeles County (LAC) and New York City (NYC) - two coastal urban areas with drastic differences in climate, topography and layout - will face from climate change and examined existing and possible mitigation frameworks. Sea level rise/storm surge and extreme heat were identified as urgent climate-driven threats to NYC, while wildfires/landslides and extreme heat were identified as urgent climate-driven threats to LAC. Despite the climate-based threats to NYC and LAC, both were determined to have employed insufficient preventative, as opposed to reactive measures. Recommendations to fix this included 1) increasing precision of climate models through accelerating research into climate “tipping points” and ensuring government agencies are using recent data and 2) bridging the climate information usability gap by i) increasing information exchange between researchers and policymakers and ii) formatting policy briefs to be digestible by legislators. Another issue was insufficient compensation for the inequitable impact of climate risks on marginalized populations; fixes included numerically magnifying the importance of these communities in cost-benefit models and establishing discourse between community members and policymakers.

Coronary Artery Disease stands as a leading cause of mortality worldwide. Early diagnosis and prompt medical intervention for cardiovascular patients hold the potential to significantly curtail mortality rates and lessen the financial burden associated with coronary artery disease treatments. Addressing the roots of cardiovascular risk factors is imperative, and Artificial Intelligence coupled with Machine Learning has emerged as a promising avenue for exploration. This study delved into the utilization of open-source Python code employing machine learning algorithms on various health parameters to predict coronary artery disease outcomes. The suggested model diagnosed coronary artery disease relying on clinical data, circumventing the need for invasive procedures. This research examined the potential of artificial intelligence machine learning algorithms leveraging an open-source comma separated values dataset from Kaggle involved in coronary artery disease prediction, assessing their efficacy, visual clarity, accuracy, and range of data. It aimed to ascertain whether the integration of these technologies will enforce a beneficial or detrimental impact on the healthcare and patient care sector. The aforementioned technologies demonstrated an accuracy level of 84.50%, with the visual clarity derived from artificial intelligence and comma separated values data being comprehensible not only for researchers and physicians to analyze, but also for patients. Artificial Intelligence driven predictive models hold promise in medical decision support systems and in addressing certain limitations that impede the effectiveness of classic regression models or traditional methods presently utilized. Subsequently, this study explored avenues for future research, where this knowledge can be used to efficiently predict coronary artery diseases.

Alzheimer’s disease (AD) is defined clinically by a gradual decline in memory. More than 6 million Americans of all ages have Alzheimer's. An estimated 6.7 million Americans aged 65 and older are living with Alzheimer's in 2023. By 2050, the number of people aged 65 and older with Alzheimer’s may grow to a projected. 12.7 million, barring the development of medical breakthroughs to prevent or cure AD. The role of genetics and environmental risk factors in AD need to be identified. People aged 65 and older survive an average of four to eight years after a diagnosis of Alzheimer’s, yet some live as long as 20 years with Alzheimer’s. This reflects the slow, uncertain progression of the disease. Identifying risk factors could ultimately help us to answer how to solve and prevent Alzheimer's dementia along with finding an effective treatment to fight Alzheimer's. The purpose of this study is to provide insight on the various genetic and environmental factors that contribute to Alzheimer's disease. The goals include identifying important genetic markers and environmental factors that contribute to the onset and progression of AD through a thorough examination of recent literature and empirical research findings. The approach entails analyzing and combining previous research to derive the main conclusions about the influence of different factors. In the end, this study aims to collect information that may guide approaches to Alzheimer's disease prevention.

Racism is a large problem in the United States, being especially highlighted in the last couple of years. Given the large history of independent schools as a leader in education with many white students coming from power and wealth, it was important to observe the impact that race/ethnicity has on inclusion in these independent schools. It was hypothesized that race/ethnicity plays a big role in students not feeling included at independent schools and the lack of exposure to cultural diversity is at the forefront of this lack of inclusion. Using a survey distributed to a group of students from independent schools, participants answered questions regarding how included they feel, how important their race/ethnicity is to their school experience, the impact of a school on cultural diversity, and understanding, acceptance, and respect of their race/ethnicity by other students. It was found that non-white students felt less included than White students and that race/ethnicity played a larger role in their school experience. Understanding of the races/ethnicities of non-white students by other students was perceived to be lower compared with that of White students. Acceptance of races/ethnicities was high across all races/ethnicities, while respect by other students of the races/ethnicities of non-white students to that of White students was lower. It was concluded that race/ethnicity does play a role in inclusion and that a lack of cultural diversity does play somewhat of a role in this cause.

In childhood, perhaps more than any other time in life, fictional narratives, particularly in the form of television and movies, are a substantial form of influence. However, children’s films can be a major source of misinformation for children and their parents alike. Despite the serious nature of food allergies, there are no studies that specifically investigate the presence of food allergies in children’s films. Therefore, this prompted the question, how are the portrayals of food allergies in the top ten-grossing children’s films that include a character with food allergies who experiences an allergic reaction during the film leading to misinformation about food allergies in children's lives? Through a mixed method content analysis, ten films that include a character with food allergies who experiences an allergic reaction during the length of the film, were evaluated over three viewings. During the film viewings, the researcher recorded data regarding the length of each reaction, how the reaction was triggered, the treatment of the allergen, key words surrounding the allergic reaction, and relevant characteristics about the character who experienced the reaction. Specifically, when it came to the treatment of allergic reactions, 5 out of the 13 (38.46%) allergic reactions were met with no treatment. Furthermore, treatment varied, including the administration of an Epinephrine Auto-Injector (Epi-Pen), administration of an unknown shot, a makeshift emergency tracheotomy while waiting for professional medical treatment, leeches, and professional care by a hospital medical team. An abundance of data revealed that, as a whole, children’s films that include a character with food allergies generally oversimplify the condition with unrealistic symptoms and a lack of medical care. Films that contain inaccurate portrayals can further spread misinformation and negatively alter a child’s understanding of food allergies; however, the films that accurately portray characters with food allergies have the potential to be used as an educational tool.

Electric propulsion refers to a type of propulsion system that uses electrically charged particles instead of chemicals or fuels to generate thrust. Electric propulsion is important because it offers several advantages to the common chemical and gas thrusters we use today, such as increased efficiency, better fuel consumption, longevity, and performance measurements. The issue this paper addresses is the complex decision of which electric propulsion system serves the best benefit in replacing cold and warm gas propulsion systems. Each system was weighed in the categories of complexity, cost, engine life, efficiency, thrust, and specific impulse (Isp). Environmental impacts are also considered. These six categories were chosen because they are often the most important factors in their implementation on spacecraft. The results show overall electric propulsion outperforms gas propulsion systems in the many categories of range in specific impulse, engine life, efficiency, and thrust with Gridded Ion Engines providing the highest mean specific impulse and resistojets providing the highest probable thrust densities. Cold and warm gas propulsion systems reigned in the categories of cost and complexity due to their simple design, manufacturing, and implementation.

Adolescents with type 2 diabetes mellitus face unique health challenges exacerbated by the widespread consumption of monosodium glutamate (MSG) and aspartame. This review investigated the potential risks and benefits of these additives to inform dietary guidelines and interventions. This paper undertook a comprehensive investigation into the impact of monosodium glutamate consumption and aspartame use on cognitive function, behavior, and weight management outcomes among adolescents diagnosed with type 2 diabetes. By synthesizing a wide range of literature and conducting a critical analysis of seminal studies, The objective of this article is to offer insights into the potential advantages and drawbacks linked to the ingestion of these food additives within this demographic. This article explored the complex interplay between dietary choices, metabolic health, and overall well-being in adolescents grappling with type 2 diabetes mellitus (T2DM), thereby contributing to a deeper understanding of dietary interventions in this vulnerable population. Sedentary lifestyles, unhealthy diets, and genetic predispositions contribute to this trend. Sedentary behavior, fueled by technology and digital devices, reduces physical activity levels.

The late 1800s was a time of great innovation and transformation in Paris. This essay explored the modernization of Paris by following two parallel developments: the widespread adoption of electric light and the flourishing scientific revolution epitomized by the work of Henri Poincaré. The objective of the study was to identify the significant historical figures, events, and policy decisions that helped Paris become a beacon of light for Europe and the rest of the world by the time of the Exposition Universelle in 1889. The research also considered what can be learned about the process of modernization more generally by studying this moment in the history of Paris—from Haussmann and his urban developments, to Poincaré’s scientific achievements, to the cultural impacts of modernity. The paper concludes that the 1889 Exposition Universelle was a turning point in history that marked the beginning of a radical transformation from Medieval Europe to our modern world—opening a pathway that has led to the technology and conveniences that society has today. Meanwhile, physics moved from a clock-work assumption of the way things work, to one with richer and more elaborate dynamics.

In today's society, issues of racial discrimination have remained commonplace, and when this issue permeated the field of education, it posed a significant challenge to creating inclusive educational environments. This study examined the prevalence, forms, and contributing factors of ethnic discrimination in schools, focusing on the perceptions of ethnic discrimination among students of different age groups and ethnicities. A survey of 133 randomly chosen participants was conducted, covering experiences of discrimination, school policy responses, and their impact on mental health and academic performance. Results revealed pervasive discrimination, with verbal harassment and stereotyping as the most common forms. Systemic issues such as stereotypes, cultural differences, and inadequate school policies were identified as major contributors. Recommendations included strong policy reforms, diversity education, improved support systems, and the need to raise awareness among students of all ages and educational institutions regarding the prevalence and dangers of ethnic discrimination.

This paper investigated how machine learning models can be utilized and combined in order to create an accurate stock market forecaster. It also aimed to use these stock market forecasts to create its own trading bot by allowing for the model to evaluate its accuracy and readjust to changes in circumstances as time progresses. This was tested through running simulations and evaluating its forecasting capabilities with metrics such as residuals and percent accuracy. A simple linear regression model and a neural networks model were combined using a self-corrective system. This system assigned weightages to the model depending on the accuracy of the model’s forecasts just preceding the predicted instance. Simulation runs were performed in which the stock market trading bot was tested on five different stocks’ data with different starting investment amounts ranging from $100 to $10000. Analyzing the data from the different combinations of investment amounts and stocks, the stock market trading bot yielded a higher than 140% return on investment for every trial conducted. This demonstrated the dependability of the models used and further highlights the accuracy of the forecasts produced. Some limitations of this program include the lack of consideration of external circumstances that affect stock data and the fact that the program only predicts prices one day in advance, all of which can be addressed in further research. However, overall the unique approach to stock market forecasting combines different machine learning models and ensures a program that is receptive to changing trends over long periods of time.

Yayoi Kusama, a transformative force in contemporary art, began her journey as an avant-garde artist in New York City during her early twenties. Her influence expanded globally in her sixties after she returned to Japan for hospitalized treatment of her ongoing struggle with obsessive-compulsive disorder. This paper delved into her background and some of her renowned artworks over this period, using art historical methods of formal analysis to underscore how Kusama’s artworks vividly depict her tumultuous childhood and ongoing hallucinations. This assertion was bolstered by a visual analysis of three Kusama artworks: No. Red B (Oil on canvas, 69” x 52”, 1960), Accumulation No. 1 (Sculpture of sewn stuffed fabric, paint, and chair fringe, 37” x 39” x 43”, 1962) and Infinity Mirrored Room – The Souls of Millions of Light Years Away (Installation made with wood, metal, glass mirrors, plastic, acrylic panel, rubber, LED lighting system, acrylic balls, and water, 113” x 163” x 163, 2013). Whereas prior research tended to focus on Kusama’s life story and her artworks, typically during a specific period of her multi-phased artistic career, this paper attempted to present how these and other factors have highlighted the presence and intersectionality of feminism, disability and Japanese influences. It also offered suggestions for further research, concluding that Kusama’s art, beyond its aesthetic appeal, had a profound transformative influence, fostering a deeper global awareness and understanding of feminism and mental health.

This investigation sought to analyze Christianity’s role in the downfall of the Solomonic Dynasty’s power in the mid- 1500s. This paper discussed Christianity’s origins in Ethiopia, its impact on political instability, and its poorly structured government systems. It argued that Christianity exacerbated the ongoing issues regarding decentralization and succession whilst also inflaming tensions between Muslims, pagans, and Christians within the region. It also discussed earlier emperors, such as Amda Siyon I, who set the foundations for Christian and Muslim tensions in the region through his conquest of Ifat and harsh policies toward Muslim subjects. In addition, the reign of Zara Yaqob is thoroughly analyzed, as his harsh conversion policies directly led to external and internal issues for the Solomonic Empire, contributing to the later Ethiopia-Adal War (1529-1543). Christianity's impact during the Ethiopia-Adal War is also discussed, as it helped create Emperor Lebna Dengel’s alliance with the Portuguese, who were driven by religious solidarity. The Ethiopia-Adal War would mark the long decline of the Solomonic Dynasty, weakening its ability to effectively govern the region. Additionally, the internal issues regarding Christianity are discussed further during the 16th century, notably the conflict with the Ǝsṭifanosites over moral practices and wealth accumulation, undermining the church’s unity and authority during the mid-1500s. This internal strife weakened the church's ability to provide cohesive leadership, contributing to broader societal instability and impacting the decline of the Solomonic Dynasty.

The United States of America uses the two-party system as its political system. However, over the last decade, the polarization of society is directly linked to the two-party system's systematic error. The two-party system polarizes the people by creating two ideas that are on opposite sides. Because of the growing issue and lack of agreement in our government, it can be concluded that the two-party system may not be the best system for America. This paper identifies the flaws within the two-party system, such as those of the Electoral College, the unfair opportunity given to third-party candidates, and the influence of wealth in elections. When comparing our system to those of foreign countries, this study demonstrates how different frameworks could reduce polarization and improve democratic representation. Findings suggest that reforms like adopting ranked-choice voting, transitioning to a national popular vote, and implementing campaign finance regulations could modernize the U.S. electoral system, promoting inclusivity.

This study aimed to identify latent profiles of child-parent attachment among Korean children and examine their relation to overall happiness. Understanding attachment during childhood is crucial, as it forms the foundation for emotional development and significantly influences well-being. Secure attachment is widely recognized as a protective factor that promotes resilience, while insecure attachment can lead to emotional difficulties and lower life satisfaction. The 12th-year data from The Panel Study on Korean Children, a nationally collected longitudinal dataset on children's development, was utilized. Responses from 1,362 children were analyzed, using six indicators for profile identification: attachment to both mother and father, with three subfactors—communication, alienation, and trust—applied to each parent separately. Following profile identification, an investigation was conducted to determine whether each profile differed regarding children's overall happiness in various areas, including academics, appearance, family relationships, friendships, school life, and daily life. Mplus Version 8 was used for the analysis. Latent Profile Analysis (LPA) revealed five distinct profiles: two securely attached groups, two insecurely attached groups (one of which was insecure specifically with fathers), and one alienated group. These profiles were compared based on children's happiness across the aforementioned domains. Securely attached children reported the highest levels of happiness across all factors, while children with insecure attachment, particularly to fathers, exhibited lower levels of happiness. This study underscores the critical role that secure parental attachment plays in fostering children's overall well-being and highlights the importance of addressing attachment issues early to support children's long-term emotional health.

Androgynous fashion has been on the rise in the late 2010s and 2020s, seen more frequently in the public eye through things like celebrities, fashion releases, and social media. This increase in prominence significantly influences and possibly destabilizes clothing’s association with gender norms. This paper’s objective was to examine the rise of androgynous fashion in the late 2010s and 2020s and the implications it has in the disruption of the public perception of gender identity. The research consisted of assessing and interpreting the history of androgyny and how it came into the public consciousness. The background of androgyny and its use historically was examined. Drawing from this framework, it looked at the rise of androgyny and its increasing prominence in the modern fashion scene. The analysis within this paper addressed the many societal factors that affect the public’s attitudes toward androgyny, including high fashion and celebrity influence. The way gender and fashion interact is addressed through the historical review of the role of women in the fashion industry and the acknowledgment of the growing number of diverse gender identities today. It also examined how the concept of gender performativity influences the potential androgynous clothing has to strengthen gender diversity and inclusivity overall and how it reshapes the way society as a whole perceives gender. This paper created a look into how androgyny has grown in the fashion scene over time, and how societal perception of gender in fashion may continue to shift in the future.

Despite the efforts of TikTok as a social media platform, social media trolling is often present in the comment sections of popular posts. Understanding how the viewers of these comments, oftentimes teenagers, perceive these comments is important in the development of intervention methods. Moreover, adolescents may experience the psychological phenomenon of cognitive dissonance after experiencing these trolls. This would result in the adjustment of beliefs or values in order to justify the derogatory comments. It was hypothesized that highschoolers who use cognitive dissonance to justify the action of trolling are more likely to perceive trolling as a minor issue. A survey was conducted to extract the perspectives of high school students on social media trolling, as well as the effect that it may or may not have on their levels of cognitive dissonance. The survey was distributed to a suburban high school in New York to be taken by students aged 13-18. A total of 176 students participated in the survey. It was found that cognitive dissonance is commonly present in teens after experiencing controversy on TikTok (p=0.042). While many teens acknowledge the negative nature of trolling, its frequency leads them to dismiss it as less harmful, suggesting a need for more effective interventions to address its psychological impact. This study contributes to understanding the psychological mechanisms behind adolescents' perceptions of trolling and highlights the importance of developing targeted strategies to reduce its effects.

Campylobacter jejuni (C. jejuni) is a pathogen responsible for the majority of worldwide enteritis. In certain cases, post-infection diseases may result in full-body paralysis or even death within the host. While C. jejuni’s natural hosts are avian species, it can infect humans through contaminated meats, dairy products, or water sources. Using whole genome sequences from national databases, an annotated phylogenic tree was created from C. jejuni and analyzed through visual diagrams. These trees revealed the scattered nature of the C. jejuni infections; while there were multiple clusters of closely related strains, clusters were often very distant from one another in terms of relatedness. They also showed multiple instances of C. jejuni infecting different species of hosts between closely related strains, which may potentially lead to its rapid spread in geographic regions and a potential increase in the number of species impacted by its spread. Additionally, it revealed a correlation between antimicrobial resistance (AMR) genes in chickens and the US geolocation. These results may be explained through C. jejuni’s ability to exchange genes and its ability to adapt quickly to its environment. As C. jejuni still remains at large in the common age, more must be understood about the nature of its infections. Thus, this study aims to explore possible correlations between host species and geographic locations of C. jejuni bacterial strains.

Accurate stock price prediction is a crucial aspect of financial markets, offering significant benefits for investors and policymakers. This study explores the application of machine learning techniques to forecast the opening stock prices of multiple companies across various sectors, including technology, energy, finance, and healthcare. Four regression models—Linear Regression, Decision Tree Regressor, Random Forest Regressor, and Multilayer Perceptron (MLP) Regressor—were employed, trained on five years of historical 'Open' prices obtained from Yahoo Finance. Model performance was evaluated using Mean Squared Error (MSE), with hyperparameter tuning conducted for optimization. Trading simulations with varying initial capital amounts were implemented to assess the practical applicability of the predictions. Additionally, the impact of significant events and political sentiment on the stock market was discussed, along with the potential of using social media data, particularly tweets, for stock price prediction. A trading strategy guided by the model’s forecasts yielded returns more than 2.5 times higher than those from simply holding the stock, demonstrating the potential increase in profitability of model-informed investment decisions. These findings highlight the significance of machine learning in enhancing stock market predictions and suggest avenues for future research to further optimize and apply these techniques in diverse financial contexts.

Cardiovascular diseases are a leading cause of death globally, making early detection imperative. This study aimed to compare various machine learning algorithms for predicting cardiovascular diseases based on parameters such as glucose levels, alcohol consumption, physical activity, and more. The primary objective was to identify the most effective algorithms for clinical prediction tasks by evaluating and optimizing their performance across key metrics. While previous research had utilized the same dataset from the University of California, Irvine, this study sought to expand on those efforts by incorporating datasets from additional sources. This broader approach aimed to provide a more comprehensive understanding of how to effectively apply machine learning algorithms in the diagnosis of cardiovascular diseases. The study employed methods such as bar plots for an initial analysis of the dataset and then experimented with every possible value for a parameter of the model within a specified range to assign the most appropriate value, maximizing the precision score of the model. The Decision Tree model achieved the best precision score among all the models, with a precision score of 78.8%, while the Multi-Layer Perceptron Classifier had the highest area under the receiver operating characteristic curve score of 0.784.

In recent years, heightened concerns over automobile sustainability have drastically increased interest in electrical vehicles (EV) in the market, with Tesla Inc. at the forefront. The company topped its earnings in 2023 with a net profit of nearly 15 billion USD— however, then failing to keep up in the EV market. Tesla’s global market share dropped from 22% in Q1 of 2023 to 17% in Q3 of 2024. Tesla now faces fierce competition from other EV companies and traditional automakers who have entered the EV market, it suffered a sharp drop in sales in the first two quarters of 2024, and its ongoing price war is driving away customer satisfaction and lowering its profit margin. This paper utilized case analysis and interviews to analyze Tesla’s market segmentation, brand value proposition, branding and marketing strategy, current global market position and competition. Tesla's brand identity is characterized by innovation and sustainability, targeting affluent, environmentally conscious consumers. Tesla’s segmentation is focused on educated, middle-aged males, primarily residing in metropolitan areas conducive to EV use. Tesla’s marketing strategy primarily relies on direct-to-customer (DTC) distribution, social media marketing, word-of-mouth marketing, and frequent price fluctuations. Given that the global competition is getting tougher, this paper suggested Tesla to diversify its marketing approaches by incorporating celebrity endorsements, increase marketing efforts to promote its full range of products, increase advertisement for its referral program, and reevaluate the direct-to-customer model by considering partnerships with franchised dealerships to support global expansion.

According to a report by the Entertainment Software Association, 64% of U.S. adults and 70% of those under 18 regularly play video games. The group of individuals who play video games is a population. A systematic literature search was conducted using PubMed and Web of Science in June 2023. After reviewing 79 articles, authors ranked health concerns related to video game players based on the number of references. This study identified 46 research articles that targeted gaming disorders, which included risk factors, prevalence, symptoms, diagnostics,  disorder mechanisms, and treatments of gaming disorders. Another top health concern for video game players is “sleep.” There is a lack of research on specific health concerns, such as mental health, vision, hearing, and nutrition. This study's findings will increase the awareness of population-based healthcare management among people who play video games, the public, health providers, and parents. The authors also discussed key issues for different audiences, such as the public, health care providers (such as Pediatrics), and parents.

Artificial Intelligence (AI) is driving a transformative shift in business, the economy, and society by altering the dynamics of interactions and relationships among companies, customers, employees, and various other stakeholders. The adoption of AI, particularly in the accounting industry, has increased significantly in recent years. This paper aims to provide a comprehensive review of the current state of AI, focusing on its applications in accounting and the challenges arising from its rapid evolution. Previous studies have contributed to the advancement of AI by conducting a thorough review of existing research and creatively applying Machine Learning (ML) and Deep Learning (DL) technologies to various tasks in accounting. This literature review examined prior research on AI applications in accounting and identified benefits and key challenges. This research suggests that the increasing use of AI in accounting has been reshaping traditional workflows, enhancing fraud detection, improving forecasting accuracy, and enabling better decision-making through real-time insights, thereby creating new efficiencies across the industry. Although these advancements have been significant, challenges such as the need for additional skill sets, concerns about data privacy, the computational complexity of algorithms, risks of bias in decision-making, and the necessity of ensuring regulatory compliance persist. This research recommends enhancing the diversity of training datasets, designing AI models to address biases, and prioritizing transparency in AI-driven decision-making to address these challenges. This paper contributes to a deeper understanding of the development of AI technologies and their extensive impact on accounting.

During the late 19th and early 20th centuries, European powers colonized Africa, profoundly changing the continent’s social, cultural, and political landscapes. African societies retained significant forms of social and cultural power despite European dominance, as Rodney (2014) reported. Africans exhibited agency through various forms of resistance, including rebellion, discontent, and support for anti-colonial movements. Notable examples of resistance include the Maji Maji rebellion in Tanganyika. However, the European powers managed to maintain control by implementing systems of forced labor, taxation, and censorship, effectively suppressing dissent. Despite their best efforts, the African elites and urban classes struggled to navigate the colonial system and exercise agency. They remained under European control, and their influence was further undermined by the establishment of indirect ruling systems imposed by Europeans. Rural and middle-class Africans, exemplified by figures such as Nweke Okafor, also showed agency within the colonial framework but were subjected to European influences through education and religious conversion. European control was solidified as it manipulated established systems, resulting in enduring impacts on African history and present-day challenges. This review article highlights the duality of African resistance and European control, emphasizing the complexities and long-lasting effects of imperialism on the continent. African society’s resistance and resilience were met with systematic oppression, which shaped Africa’s historical trajectory and ongoing struggles.

Within society, it is common to see the repercussions of how one's outward appearance impacts the treatment they receive from others. The exploration of the extent to which certain attractive characteristics impact the outcome of an arraignment identifies if defendants fall victim to these repercussions. Past studies have alluded to the issue: the defendant's outward appearance, in some cases, has more weight on the judge's decision-making than the crime itself, expressively illustrating how a judge's potential bias may sway their decision of an outcome. Historically, defendants' outward appearance has been seemingly judged, as evidenced by scholars Judith Langlois and Charles Frazier, leading to the question: to what extent is the outcome of an arraignment influenced by certain attractive characteristics of a defendant's outward appearance? By addressing this question, one could determine if a judge’s bias influences the outcome of an arraignment. It was hypothesized that defendants who possess more than half of the attractive traits identified by David Frederick and Anthony Little would be released on recognizance while defendants who possess less than half will be incarcerated. To execute this aim, observational research was conducted within an arraignment courtroom by evaluating if defendants possessed light skin, small facial features, ovular face (in women), sharp jawline (in men), symmetry, averageness, clear-looking skin, and slim body appearance. After, the corresponding outcome was recorded within a printed checklist. In total, 30 defendants were observed and a Chi-Square Test was completed to analyze the data. From the Chi-Square Test, it was found that there was a P-value of 0.0730 percent, therefore making the results statistically insignificant, meaning that the eight characteristics evaluated had no impact on the outcome of an arraignment. These results suggest that the outcome of an arraignment is determined by the facts of the arraignment rather than the characteristics of their physical appearance.

Unintentional injury and violence are important public health topics for youth. This study aimed to discover prevalence of risk for injury and violence and explore statistically significant differences by sex and race. Hypothesis is males are at higher risk for injury and violence and minority populations are also at higher risk for unintentional injury and violence. Quantitative analysis was performed to determine any association of injury or violence with race or sex. The Youth Risk Behavior Survey data includes questions regarding unintentional injury and violence: Differences by sex and race were analyzed for unintentional injury and violence among youth. Significantly more females reported being electronically bullied compared to males. There was a difference between females and males in youth reporting they were ever physically forced to have sexual intercourse. Females were more likely than males to report they did not go to school because they felt unsafe at school or on their way to or from school. Nearly 40% of all youth reported not always wearing a seat belt when riding in a car driven by someone else. Black youth reported statistically significant higher percentage than all other races in not always wearing a seat belt when riding in a car driven by someone else. Black youth (8.5%) reported highest percentage of being in a physical fight at school followed by multiple race youth (7.6%). White students were more likely to be bullied on school property compared to Blacks or Asians. Asian youth were less likely than other race groups to be threatened or injured with a weapon on school property. Hispanic, Black, American Indian/Alaskan Native youth were more likely not to go to school because they felt unsafe at school or on their way to or from school compared to white youth. Asian students were less likely than other race groups to have ever been physically forced to have sexual intercourse. American Indian/Alaska Native students were more likely than all race groups to have ever been physically forced to have sexual intercourse. These results indicate that, in an effort to reduce violence and unintentional injuries to youth, it is important to consider sex and race, to assure that prevention resources are directed to those at greatest risk. In real world school settings, these findings can be used to develop anti-bullying activities that incorporate understanding of different races/cultures, strengthen empathy for those who are different, reduce stigma and racial stereotypes. Schools can incorporate antiviolence, anti-bulling programs that raise awareness about the racial and gender differences that exist.

Small business events, non-profit organization events, and many small conference events are still struggling to organize their events due to their limited budget and the lack of IT skills. The chatbot could be one of the potential solutions because it can guide event participants without hiring human resources to interact with potential and actual customers. The chatbot powered by the generative AI are used in a variety of industries like the customer service, e-commerce, healthcare, education, and so on. To use the chatbot with these events, different categories of artificial intelligence were reviewed to determine the chatbot's classification. Research was conducted to identify efficient and stable methods for implementing the chatbot. To facilitate easy and rapid development, the Python programming language was selected, along with the Flask, LangChain, and Zappa Python libraries, which were chosen for web interface development, integration with the existing large language model (LLM), and AWS deployment, respectively. The significance of LLM and LangChain to the chatbot application was analyzed. The chatbot was tested with two websites and deployed in the public cloud using AWS. Additionally, a price range for the chatbot application was provided based on the gpt-3.5-turbo model in OpenAI, along with estimates of potential usage volume. This chatbot application aims to help non-profit organizations reduce their workload and budget for customer communication.

Korean shamanism, also known as muism or mugyo, is one of the oldest continuing spiritual practices in the Korean peninsula. In this paper I examine mugyo and attempt to develop a contextual overview of its ritual components, focusing on a comparison of regional variations in Korean shamanic rituals as well as the various aesthetic elements that are commonly present in these often costly, time-consuming, and elaborate ritual performances. I attempt to provide a comprehensive overview of Korean shamanism, considering various perspectives to contextualize its cultural and ritual development. The history of Shamanism in East Asia dates back to prehistoric times, and is characterized by a belief in spirits that take various forms—often as ancestors, local folk deities, natural phenomena, and so on—and the role of shamans as intermediaries between the human and spirit worlds. Korean shamanism developed in conversation with Buddhism, Taoism, and Confucianism. Despite historical persecution, Korean shamanism remains integral to Korean culture in the modern day. Shamans are consulted for health, relationship, and business issues. Modern media often depict shamanism as both mysterious and healing. Korean shamanism is a resilient evolving practice that is closely woven into the fabric of Korean culture. This research is important in that it gives an accurate view of the spiritual practice, of which current scholarship is greatly lacking. By the end of the paper, the reader will gain a better understanding of shamanism as well as its significance to traditional Korean culture.

The growing severity of the climate crisis has led many technology companies to pursue sustainability, which has often been done through the recycling of materials. However, the material that is recycled often varies as companies choose to recycle either plastic or metal. With such variation, firms are faced with a pressing issue on which recycling method to pursue, so that they can ensure a shift towards sustainability while retaining profitability. Thus, the aim of this paper was to compare the environmental and economic consequences of plastic and metal recycling for technology products to determine the situations in which each would be most logical. The methods for this paper entailed qualitative research and environmental accounting, which have determined that metal recycling, in terms of fixed and marginal costs, is cheaper than plastic recycling and is overall more sustainable. However, plastic recycling may lead to greater consumer satisfaction. Recycling metal is most logical for companies who value environmental impact and smaller companies who cannot afford plastic recycling. Plastic recycling is most logical for companies who want to promote their brand and gain more customer loyalty. As a result of these factors, plastic recycling remains popular despite metal recycling’s many benefits. Further adoption of metal recycling can be encouraged through more public discourse, investment in newer recycling technologies, and stronger advertisements for metal recycling. Metal recycling can also be encouraged through carbon legislation, such as carbon taxes or tax deductions on sustainable practices. Overall, this research demonstrates the need for further action to promote metal recycling. 

Amidst a post-pandemic world, understanding the mechanisms of associative learning and anxiety can offer crucial insights into the fundamental processes underlying the human mind, paving the way in a globe where mental health issues have surged globally. This paper explored how anxiety affects associative conditioning and behavior, introducing a computational model simulating zebrafish behavior. The research aimed to address how zebrafish respond to visual and auditory stimuli by associating cues with rewards or punishments, providing insights into learning processes. Through tracking movement and a modeled anxiety level influencing decision-making, the simulation revealed patterns in zebrafish behavior, highlighting the roles of anxiety and cues in associative learning. The findings underscored key insights into the neural mechanisms underlying learning and attention, while also identifying potential applications for understanding anxiety disorders and survival strategies within our own species. Ultimately, this research provided a framework for applying similar models to other animals and humans, rendering it an invaluable asset across diverse scenarios.

Mental health disorders such as depression, anxiety, and Attention-deficit/hyperactivity disorder (ADHD) affect millions of individuals worldwide. To address these challenges effectively, innovative solutions are needed that provide timely, accessible, and personalized support. This paper presents MangoBot, a prototype robotic companion that integrated 3D-printing, programming, animation, and AI-generated music. The development of MangoBot was motivated by research highlighting the therapeutic benefits of art forms like music and animation, alongside the emerging robot-assisted therapy. Many existing humanoid robots lack the ability to adapt to individual needs. MangoBot fills this gap by offering an open-source platform that enables extensive customization. This flexibility allows for the integration of AI and diverse technologies to generate personalized content and enhance human-machine interaction. Designed for children and teens dealing with anxiety, depression and concentration challenges, MangoBot serves as a personal companion to support emotional well-being. MangoBot was developed by a team of five middle school students mentored by their parents and coaches. MangoBot’s 3D-printed humanoid body is powered by a Raspberry Pi and utilizes Tkinter for the graphic user interface, DeepFace for facial recognition, and Sono-AI for music generation. MangoBot can play personalized music, animate eye movements, and even dance, offering an engaging and interactive experience tailored to the emotional needs of users. The design process was iterative, with continuous improvements based on feedback from neuroscientists, counselors, healthcare professionals, and users. Early qualitative analyses revealed positive responses from children, who demonstrated a strong connection to MangoBot during short-term engagements at robot competitions, STEM conferences, and other outreach events like technology and career days. This project demonstrated that humanoid robots, particularly those that are affordable and customizable, have significant potential to support mental health and well-being. MangoBot represents a promising step toward bridging a critical gap in mental healthcare, offering personalized, interactive support for those in need.

As humanity sets its sights on establishing habitats beyond Earth, ensuring sustainable space settlements becomes a crucial goal. Space presents unique and harsh conditions that require power solutions beyond the capabilities of traditional terrestrial power systems. This paper explored the critical factors, technologies, and construction techniques necessary for successfully implementing microgrid electricity systems in space settlements. The paper introduced several key technologies in the development of space microgrid systems, including wireless power transmission, advanced energy storage solutions, modular construction techniques, and smart grid technologies. Each of these technologies was explored in detail to demonstrate how they contribute to the overall sustainability and efficiency of space microgrids. Key findings highlighted the advantages of integrating these technologies to improve energy efficiency, stability, and resilience in extraterrestrial settings. Potential applications included the use of space-based solar power, nuclear fission, and hybrid systems to ensure continuous power availability, the use of supercapacitor to improve power storage systems, the use of various construction technologies. These results underscored the importance of microgrid systems for the successful establishment of sustainable space settlements.

Fear is a natural response in the presence of danger, orchestrated by the amygdala, a key region in the brain for processing fear. The amygdala triggers the “flight or fight response” for multiple fears and phobias. Throughout this research paper, the effectiveness of different treatment methods for fears is explored by analyzing heart rate data from a FitBit. There is limited research on the efficiency of multiple fear therapies, and there is a lack of direct comparisons between fear therapies. This research paper seeked to address the problem by directly comparing three different fear treatment methods. Types of fears tested include jumpscares, heights, oceans, bugs, no control during skiing, and darkness; using 17 people for the 3 different therapies: Visualization Therapy, Exposure Therapy and Cognitive Behavioral Therapy. For the comparative analysis, percent change was used in heart rate and used a different person for each treatment. The conclusion was that Cognitive Behavioral Therapy had the most profound effect in the experiments in treating fear as measured by the change in heart rate. It is shown that this was the most effective therapy because it includes a multi-step approach, which features an increase in “scariness” for each round of treatment, thus allowing for the brain to adapt and adjust.

This study explores various effects of stress-induced anxiety and depression on neurotypical and autistic adolescents within the limbic system. Through the outcome of the study, the groundwork was laid to effectively bridge the gap of required stress treatment for autistic adolescents. The research utilized the methods and procedures of a systematic review. There were several major differences in how the autistic and neurotypical brains respond to anxiety and depression disorders such as generalized anxiety disorder and major depressive disorder. These included levels of cortisol secretion, amygdalar structure and activity, and hippocampal volume. In individuals suffering from depression with autism spectrum disorder (ASD), amygdalar structure decreased in volume while encountering blunted activity in neurotypical individuals. Looking at cortisol activity, its levels were found to be inconsistent and dysregulated. On the other hand, in neurotypical individuals, hippocampal volume decreased as well as a decrease in gray matter volume in this region. While the components of the limbic system affected are similar, their structural, volumetric, and/or functional changes are variable, which caters to the fact that ASD varies individually. Along with this, the study brings light to the severity of depression and anxiety in adolescents with ASD compared to neurotypical adolescents.

Despite the increasing focus on mentorship programs designed to support women’s success in the workplace, there is a lack of understanding of how well these programs align with the allyship behaviors women find most impactful. Critical allyship for women are underrepresented, raising concerns about the effectiveness of current mentorship initiatives in promoting gender equity and highlighting the need for further research to ensure that mentorship programs can better integrate female-centered allyship behaviors. Female-centered allyship behaviors should be aligned to mentorship programs if the programs are designed to help women become successful. 8 mentorship program manuals in various fields were analyzed for allyship behaviors using a scale of -1 to 1 based upon prominence and relativity. T-tests were performed to test whether women-considered or men-considered allyship behaviors were more strongly aligned and determined which allyship behaviors were most prominent. There was no statistically significant difference in frequency of men-considered and female-considered allyship behaviors. The most prominent allyship behavior was providing an opportunity to speak and the least prominent allyship behaviors were providing a male presence, savior attitude, discrimination confrontation, and asking for equal treatment.

In recent years, with the release of a new controversial math framework, there have been widespread concerns over how to reduce achievement gaps between demographics and increase the accessibility of math education in California. This study served to improve our understanding of the math proficiency of California high school students, by comparing California and nationwide data on the AP math exams. A secondary analysis of College Board AP test data and U.S. Census data from the year 2020 found that California students were more likely to participate in AP math exams. However, performance on these exams was below the nationwide standards for students in historically low-performing demographic groups. Additionally, a comparison between math AP exam data and the three most popular humanities AP exams showed that the gaps between demographics were larger in math than in humanities. Overall, the analysis suggested that more educational support needs to be provided specifically for California’s large Hispanic demographic and in the subject of math. This research holds significance in helping education policy makers determine what is being done well and what issues still need to be addressed in the California education system.

In the past two years, a significant portion of the undocumented population entering the US via the Mexican American borders has been Chinese. However, their lives and experiences have gone unnoticed. This study contributed to the source material on migrant illegality that is primarily based on the experiences of Latinx migrants and single adults. It highlights the cyclic and unique struggles faced by undocumented Chinese migrants and their families, especially in how such struggles impact the youth and leave them in a fixated vicious cycle. So, adjustments to immigration policy are a necessary way to address this problem.

This research paper covered the current understanding of possible advantages the SpeedoⓇ LZR Racer and other tech-suits, or technical suits, have over other swimsuits. More specifically, it aimed to investigate the legitimacy of FINA’s(Fédération internationale de natation) ban against tech-suits. Insight into this topic is necessary since Speedo and other companies faced considerable financial losses due to their swimsuits getting banned for technology doping by FINA, now known as World Aquatics. In other words, the tech-suits were banned due to their excessive advantages over other swimsuits. This research showed that the advantages of the SpeedoⓇ LZR Racer and other tech-suits were apparent as these full-body tech-suits showed decreased drag coefficients and higher velocity due to the compression of the swimmers’ bodies. Moreover, the research also exposed that the SpeedoⓇ LZR Racer does not have fabrics that resemble sharkskin through microscopic images. The research also discussed the possibility of  biological advantages of the SpeedoⓇ LZR Racer and other tech-suits such as the increase in ATP production and activation of fast-twitch fibers. In conclusion, this research concluded that tech-suits does offer a considerable advantage to athletes in competitive swimming, justifying the ban and the nullification of records broken with this suit. This conclusion and the justification behind it can be applied to various areas especially in future regulations on technology doping and future developments of high performance gear.

JRHS Outstanding Research Paper Award

Hepatocellular carcinoma (HCC) poses a pressing global health challenge, ranking as the third leading cause of cancer-related mortality worldwide. However, the intricate molecular mechanisms underlying its onset remain elusive. The primary objective of this study is to identify novel genes associated with the disease progress of HCC. Using data from the GEO database, differential gene expression analysis was conducted using the online GEO2R tool, followed by functional enrichment analysis. A total of 1,729 upregulated and 1,131 downregulated genes were identified in HCC compared with the adjacent normal tissues. PSRC1 was selected for further analysis owing to few reports in liver cancer. PSRC1 was significantly upregulated in HCC, which was supported by either bulk or single cell RNA sequencing data. PSRC1 was mainly expressed in hepatocytes and tightly correlated to clinic staging and tumor grades. High expression of PSRC1 predicted poor prognosis. The current study provided new insight into the occurrence and development of HCC.

The research paper "Urban Evolution: How Species Adapt to Survive in Cities" by Eric Bender parallels the adaptations of urban wildlife with the cognitive challenges faced by Chinese adolescents in the digital era, specifically through cryptocurrency. This study addressed the problem statement of how cryptocurrency influences the cognitive development of Chinese adolescents amid rapid technological changes. The objectives of the research are to examine both the educational and economic benefits of cryptocurrency and the negative cognitive impacts associated with excessive engagement. Using a scoping review methodology, the research examines a wide array of sources to provide a comprehensive understanding of cryptocurrency's impact. The findings revealed that cryptocurrency offers economic benefits, such as portfolio diversification and stimulating interest in new economic models. However, excessive engagement can disrupt sleep, lead to social isolation, and impair decision-making, similar to behaviors seen in Internet Gaming Disorder. Professionals exhibit cautious engagement with cryptocurrency, recognizing its risks, whereas adolescents are more vulnerable to financial losses and scams. Restrictive Chinese government policies further limit adolescents' exposure and understanding, potentially hindering their preparedness for future financial environments. The study concluded that while cryptocurrency has educational and economic potential, the associated cognitive risks and the impact of government restrictions necessitate structured education and guidance. Addressing these challenges is crucial to mitigate adverse outcomes and ensure adolescents can effectively navigate the evolving digital and economic landscapes.

As universities and creative industries grapple with the integration of Artificial Intelligence (AI), questions surrounding legitimate AI use versus unethical practices remain prevalent. While some institutions view AI as a novel tool for enhancing teaching, learning, and creativity, others are concerned about its role in facilitating academic and artistic dishonesty. The nuance of this argument is further complicated by the newness of the technology and the lack of clear ethical guidelines. This paper delved into the complexities of AI's impact and explored the blurred lines between legitimate AI assistance and plagiarism across various industries. Drawing parallels across domains such as academia, music, visual arts, journalism, and law, the paper scrutinized the ethical and legal implications of AI-generated content. Discussions ranged from the attribution of training data to AI models and the ownership of output to the potential for AI to disrupt established creative industries. Legal precedents, such as those in the music industry regarding sampling, fair use, and copyright infringement, provide insight into the evolving landscape of AI regulation. In journalism and law, contrasting attitudes toward AI utilization highlight divergent perspectives on professional ethics and AI's role in research and content generation. Moreover, recent lawsuits against tech companies highlight the contentious nature of AI's use of copyrighted material for training purposes. The paper concluded by advocating for an approach that balances technological advancement with ethical responsibility. The paper emphasized the need for robust legal frameworks and institutional policies to govern AI use while protecting creative innovation and academic integrity. Fair use, as used in this paper, refers to the legal doctrine that says brief samplings of material in certain situations can be used without consent from the copyright holder. Ethical responsibility, as used in the paper, can be defined as the ability to determine and act on what is fair or just.

This paper provides an overview of Spotify's current market position as the global leader in music streaming and outlines strategic recommendations for sustaining and enhancing its competitive edge. Despite facing challenges such as profitability and emerging competition from tech giants, Spotify's user-friendly interface, innovative features like real-time lyrics and Spotify Connect, and personalized recommendations have contributed to its impressive growth, reaching 574 million monthly active users and generating over 11.7 billion euros in revenue by 2022. To maintain its market leadership, the paper suggests the implementation of a marketplace for non-fungible tokens (NFTs) to foster deeper artist-audience connections and unlock new revenue streams, alongside exploring Spotify-branded hardware to enrich the user experience. Additionally, a strategic price adjustment is recommended, raising the individual premium subscription fee by one dollar to $11.99, to leverage Spotify's brand loyalty and increase revenue without significantly impacting subscriber growth. These initiatives are projected to bolster Spotify's financial sustainability, enhance user engagement, and ensure the platform's continued dominance in the evolving digital music landscape.

Oil spills are frequent accidents that have detrimental effects on the environment and health of organisms. To minimize environmental damage, oil spills require immediate cleanup, however pre-existing methods such as booms and skimmers have limited abilities. Natural sorbents are environmentally friendly, higher in sorption capacities, and inexpensive compared to conventional synthetic methods, but many tested possibilities lack hydrophobicity. The potential of human hair was investigated using adsorption experiments in kerosene and petroleum ether respectively. Human hair was transformed into a novel composite material coated with trichloro(octadecyl)silane (TCODS), a hydrophilizing agent shown to increase oil-water separation. Curly and straight hair were compared in both composite and loose form along with the presence of TCODS to test the sorption ability of the composite material given its greater surface area. The order of highest efficacy in oil removal to least performing sorbent modification was the loose with TCODS, loose itself, composite with TCODS, and composite itself. Decreased sorption capacities of the composite material can be attributed to decreased buoyancy of the starch solution, which resulted in a nonuniform, blocked cuticle. On average, curly hair adsorbed significantly more oil than straight hair due to its greater natural porosity. Comparison of sorption rates showed that a short contact time is required to attain a majority of total oil adsorbed. Reusability test demonstrated that human hair can be utilized several times without deterioration in sorption characteristics. Overall, this study supports the potential of human hair as a low-cost sorbent for oil spill cleanup.

Bone plays a pivotal role in safeguarding organs, supporting bodily structures, and maintaining calcium homeostasis. Injuries to bones or bone disease often necessitate interventions for bone repair, such as bone grafts. Autografts and allografts are currently the most promising techniques, but they exhibit limitations, which prompts the exploration of alternative solutions, particularly artificial bone graft materials. This paper examined the potential of hydrogels as a groundbreaking alternative to traditional bone graft materials. In order to properly assess the promise of this new technology, recent advancements in hydrogels are explored here, paying particular attention to whether hydrogels are capable of overcoming the limitations of current bone grafting methods. As a result, hydrogels show significant promise as bone graft materials due to their mechanical strength, biocompatibility, and unique properties like injectability and temperature sensitivity. They can effectively fill irregularly shaped bone defects and support bone regeneration by mimicking the natural extracellular matrix and incorporating bioactive elements like Mesenchymal Stem Cells (MSCs). Their potential for minimal invasiveness and gradual degradation offers advantages over traditional bone grafts. However, challenges such as variable performance and experimental limitations still need to be addressed through further research. Hydrogels yield promising findings due to their helpful and unique qualities in aiding bone regeneration. Hydrogels demonstrate strong potential as bone graft materials due to their mechanical strength, biocompatibility, and injectability, allowing for minimally invasive application. They effectively support bone regeneration by mimicking the natural extracellular matrix and incorporating bioactive elements such as Mesenchymal Stem Cells (MSCs). Their ability to degrade gradually as new bone forms offers an advantage over traditional grafts, though further research is needed to address performance variability and optimize their use. By comparing the latest research findings, this report highlights the potential of hydrogels in revolutionizing the field of bone grafting. The injectable nature of hydrogels, coupled with their ability to serve as carriers for cells, drugs, or proteins, puts them in a promising field for future bone regeneration techniques, offering a safer and less invasive alternative to traditional interventions.

The distance to a star is one of the most fundamental pieces of information that can be calculated about it. Currently, several methods exist for calculating distance; however, many of them are quite inefficient and prone to error. To combat this, one could classify to find its properties and subsequently use the classification to calculate the distance. Tools exist to classify stars, but lack in terms of accuracy and ease of use. To make measuring distance more efficient and accessible for professional and amateur astronomers alike, we developed a novel method for measuring stellar distances using spectroscopic parallax and machine learning. The procedure of this project goes in two steps: first, to utilize Google’s MobileNetV2 to identify a star’s spectral type on the Morgan-Keenan System with 97.9% accuracy, and second, to use these classifications to calculate the distance to the star with relatively low error. Classifying a star into a Morgan-Keenan System provides data on the temperature and color of a star through spectral class and size, luminosity, and evolutionary phase through the luminosity class, and by combining this with Hertzsprung-Russell Diagram data, we may calculate the absolute magnitude, or how bright the star is at a standardized distance from Earth. Using the image itself, the apparent magnitude, or how bright the star looks from Earth, can be calculated. The distance can then be calculated by comparing the absolute and apparent magnitude using a method called spectroscopic parallax. Applying computations to this method makes large telescopes much more cost-efficient.

A single-chip Application-Specific Integrated Circuit (ASIC) for text-to-Braille conversion has been proposed to address the limitations of existing solutions, such as limited translation abilities, high costs, and low reliability. This chip is designed to support both electrical (via light-emitting diodes) and mechanical (using push-pull solenoid actuators) Braille character displays. The ASIC integrates various components, including a memory block, a character size calculator, a mapping table, a converter, and a reader, all developed using the Verilog hardware description language (HDL). It is capable of simultaneously displaying up to 8 Braille characters and introduces a novel feature—reading pace control—to enhance usability. The text-to-Braille conversion function has been successfully simulated in Verilog and verified through Field-Programmable Gate Array (FPGA) implementation. The chip is fabricated using the SkyWater 130nm Complementary Metal-Oxide-Semiconductor (CMOS) process, with 5 metal layers, through the open-source Tiny Tapeout program. This solution provides a fully-translatable, cost-effective, and mass-manufacturable design, serving as a scalable logic processor base that can be expanded to display additional Braille characters concurrently.

Artificial intelligence integration has ushered in a new era of efficiency and complexity in the world of modern finance. One of the most notable examples of AI in the financial markets is algorithmic trading, which has fundamentally altered how traders operate and how markets behave. The ethical implications of algorithmic trading must be examined as financial systems adopt new technology, posing concerns about accountability, equity, and transparency. This study delved into the complex interplay between ethical considerations in financial markets and algorithmic trading practices. The growing impact of AI on consumer behavior and the pressing need to consider its ethical implications which spurred me to conduct this investigation. The purpose of this study was to investigate the following question: How does algorithmic trading impact stock returns, and how does this impact pertain to ethical principles in finance? The following sections offer a comprehensive literature review that puts my investigation into context and sheds light on previous studies on algorithmic trading, financial ethics, and their relationship. I used statistical methods to give the investigation a quantitative component and enable me to determine the degree of uncertainty surrounding our conclusions thanks to the supporting confidence interval. In the conclusion, the paper provided an overview of the main findings, explored their implications for financial ethics, and offered recommendations for further research in this rapidly developing area. As I continue to examine the broader implications of technology, finance, and ethics, this research contributed to a detailed understanding of how algorithmic trading shapes financial markets and the ethical considerations that accompany these changes.

With the new trend of AIs popping up everywhere, the stock trading game has changed. Lots of stock traders have pivoted and started using AI to make educated decisions to maximize profit on stocks based on the data given to the AI model.  This research explored the effectiveness of AI models called Linear regression and Multi-Layer Perceptron (MLP) regression for predicting stock prices. Utilizing historical stock price data from Cisco (CSCO), Tesla (TSLA), Apple (AAPL), Starbucks (SBUX), and Johnson & Johnson (JNJ), the study evaluated the models' performances based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results offer valuable insights for investors, suggesting that the choice between these models should be guided by specific trading strategies and practical considerations rather than solely performance metrics shown here.

This study delves into the different influences of the media during the twentieth century. Throughout history, the media has served as a tool for communication to connect people globally. Most notably, the American media played an especially large role during this time, as its increasing influence shaped the public opinion of its citizens. In World War I, the media’s portrayal of events led to a shift in sentiment from neutrality to a strong support for war, influencing President Woodrow Wilson's decision to enter the conflict. The establishment of the Committee on Public Information (CPI) further highlighted how the media was utilized to unite the public while also marginalizing German Americans through propaganda efforts or information spread to influence public opinion. Throughout World War II, the collaboration between the Office of War Information (OWI) and media outlets aimed at spreading Japanese propaganda, promoting racial prejudice, and supporting harsh measures against Japanese Americans, including internment and the deployment of the atomic bomb. This analysis illustrates that although the media was instrumental in rallying support for war efforts, it often did so at the expense of the truth and also marginalized communities. Consequently, stricter broadcasting ethics emerged in the second half of the twentieth century. Thus, this study dives into the changes within the American media following the war, highlighting significant developments in its approach and influence.

This research aims to address the challenges farmers face in optimizing crop yields and managing resources sustainably. The study focuses on Croptimization, a machine learning-based platform designed to improve crop selection and farming practices. The key research questions are: How can machine learning be used to predict optimal crop choices? What impact do environmental factors have on crop performance? The study's objectives are to develop a model that provides personalized crop recommendations and to assess its effectiveness in different farming contexts. The problem lies in the unpredictability of traditional farming methods, which often do not consider dynamic environmental conditions. The study utilizes linear regression, Lasso Regression, and Decision Trees to analyze local weather patterns and soil characteristics. The findings show that Croptimization can accurately predict crop performance and offer valuable insights for farmers. This research is significant because it contributes to sustainable farming practices and global food security.

Colorectal cancer (CRC) occurs when colon or rectum cells undergo uncontrollable growth. One cause of CRC can be attributed to mutations in the MUTYH gene. This gene codes for a protein, MYH glycosylase, that participates in a DNA damage repair pathway. Individuals with biallelic MUTYH mutations are considered to have MUTYH-associated polyposis (MAP). MAP can be characterized by the development of many adenomatous colon polyps, precursors to colorectal cancer. The failure of MYH glycosylase to perform its proper DNA damage repair function leads to hypermorphic mutations in many other genes, causing increased cell proliferation and polyposis. Genetic testing of these target genes along with early and frequent forms of detection, like colonoscopies, to detect the presence of adenomatous colon polyps are truly the keys to managing the risks of CRC. Colectomies, or the surgical removal of precancerous colon polyps, could be performed to treat CRC if necessary. Many new treatments and procedures are also being researched, including new forms of CRC detection and surgical procedures that can mitigate the effects of CRC malignancies. This inquiry aimed to determine whether or not CRC is currently being managed in this best way possible in the healthcare industry through a wide survey of reputable scientific literature. This study explored current and emerging treatment options to mitigate the effects of CRC, drawing on clinical and preclinical work in the field. Data and statistics were used to fortify and expand upon findings. The findings presented in this study reveal inefficiencies in the way CRC is managed and provide potential solutions to amending these issues.

As Hematopoietic stem cell transplantations (HSCT) are high-risk procedures with a 30-50% likelihood of resulting in Graft Versus Host disease (GVHD), a critical complication that significantly impacts patient outcomes, it is essential to have an effective treatment. However, current efforts to find specific treatments result in low response rates. Understanding the interactions that cause host rejection, GVHD, and a successful transplantation is imperative. This review highlighted innovative therapies such as HLA matching and typing, Antibiotic Prophylaxis, etc. which utilize personalized medicine to increase success rates. Ethical issues such as the risk of adverse reactions due to drugs which were tested in unconventional clinical trials, mishandling of genetic data in commercial genetic testing companies, the wellbeing of an infant produced via preimplantation HSCT typing, and whether the cost of treatment would allow equitable access should all be considered. The challenge of balancing ethical rights with public health interests, especially in the context of genetic testing and research, requires thoughtful consideration. Despite this, the potential of personalized medicine in HSCT is paramount. By implementing some of the following personalized treatment strategies, an increase in success rate along with longer lifespans had been recorded. Nevertheless, fatal side effects such as Cytokine Release Syndrome (CRS) question their integration into mainstream therapies. As precision medicine continues to advance, it is crucial to provide a substantial trial period and find strategies to create a confidential environment. Future research should focus on refining these personalized procedures and exploring their broader applications in the field of HSCT.

The formation and evolution of galaxies are key processes that shaped the heterogeneous universe from a homogeneous state after the Big Bang. The advent of high-resolution space telescopes has provided unprecedented detail in observing distant and faint objects, enabling deeper analysis of galaxy morphology and structure that builds on the foundational classifications established by the Hubble Sequence. Aiming to study the galaxtic evolution in the Southern Massive Cluster Survey (SMACS) 0723 and identify the age of the more complex disk galaxies, this work examined the morphology of galaxies in using data from the James Webb Space Telescope (JWST). The captured high-resolution images provided new insights into galaxy formation and evolution, revealing a larger number of galaxies than previously visible with the Hubble Space Telescope. By applying the Morpheus machine learning model, 755 galaxies were identified and classified into four morphology categories: disk, spheroid, irregular, and point source. With more than 80% of the classified output receiving high confidence scores, this deep learning method was shown to be effective at analyzing JWST's high-resolution images, providing clearer and more detailed insights into galaxy morphology. The distributions of all types of galaxies throughout this cluster were found to be homogeneous, with the majority classified as disk galaxies. Additionally, by aligning JWST data with photometric redshift information from the Reionization Lensing Cluster Survey, this study presented a correlation between galaxy morphology and redshift; a substantial number of disk galaxies, over 50% of all galaxies, were identified at z > 4 for the first time. These findings suggest that disk galaxies have existed for billions of years, challenging previous assumptions about galaxy evolution. This research emphasizes the importance of JWST in advancing understanding of the universe and lays groundwork for future studies on galaxy morphology and evolution.

The prevalence of online newspapers and media sources has been coupled with the growth of accessible retail investing throughout the U.S.. Although there has been an increase in forecasting, there are no conclusive determinations regarding the influence of media on stocks and the broader market. The lack of current literature surrounding this contemporary topic of news media’s relationship with the Market Efficiency Hypothesis necessitates new research. To assess the degree to which the media is able to affect the stock market, articles pertaining to Apple and S&P 500 from 7/28/22 to 2/03/23 were selected from the Wall Street Journal and Barrons. Each article was given a positivity rating, ranging from Extremely Positive to Extremely Negative, and was compared to the change in the corresponding stock’s price. The research revealed a weak correlation with Apple, which had less data, and a mildly strong correlation with S&P 500. Using the correlation coefficient equation and statistical analysis, a 0.75 correlation factor was determined for S&P 500. Thus, these findings refute the Market Efficiency Hypothesis, showing how the stock does not trade at its “fair” value, and how modern factors such as instantaneous news and media influence its price. Overall, as news platforms continue to increase viewership, along with the ubiquity of social media platforms such as X, Reddit, and TikTok which continue to drive investing popularity and regularity, the influence that media has on the market will become paramount as new technology emerges.

One of the many fascinating features of the brain is the ability to form motor habits, known as “motor memory”. Following a revolutionary 1997 study, the topic of motor memory consolidation, or how the engram changes over time, began to garner significant attention, and the field has only grown since. The first objective of this review was providing an overarching synopsis of this topic’s history, mainly the debate on whether motor memory consolidates and current gaps in knowledge. The second goal was to identify and provide evidence for one particular consolidation model, including acquisition and storage sites, believed to be the most well-supported. To conduct the research, Google Scholar was the main database utilized, and most articles before 1997 were eliminated. The first finding was the general mechanism of consolidation: similar to declarative memory, the motor engram after encoding transforms from an initially fragile state to a stronger one resistant to disruption. The second finding was related to the proposed acquisition sites, such as the cerebellum and prefrontal cortex. This paper contended for the former, as the cerebellum’s significance in acquisition has been demonstrated through vestibulo-ocular reflex training and Pavlovian eye conditioning. This paper also identified studies that discuss long-term storage areas, such as the vestibular nuclei. Finally, existing issues like the complexity of the encoding mechanism, effects of sleep fragmentation, and the factor of age were addressed. This review contributes towards a more unified consolidation model to be used in various real-life applications, such as sports science.

Minimally invasive surgery has become standard for many medical operations. However, it comes with its challenges. For one, surgeons aren’t always skilled. While some may be incredibly experienced, operations are still susceptible to human error—especially with rigid instruments. With new advents in soft robotics, soft robots are being explored for optimal surgery, better patient experience, and more effective diagnoses. The surgeons will no longer be responsible for instrumentation with soft robots, they mainly help with robot orientation, diagnoses, and interpretations of data provided by the robot. Soft robots are safer and more human-friendly, with gentler materials as well as fluidity that makes them apt for many circumstances. However, there are some issues in controlling their movement. In this paper, a learning-based kinematic controller was presented as the solution for a generic soft robot through a simulation. This model proved that it is possible to control a soft continuum robot through traditional techniques used for rigid robots. These techniques are optimized for soft robots through modern methods like machine learning by providing it data from motor babble. This discovery can be utilized for future models of soft robots built for minimally invasive surgery or other extensive uses. This will further development in the soft robotics field.

This paper studied the effect of a venture capital (VC) firm’s reputation and social media presence on the portfolio company’s post-IPO long-run performance based in the US between the years 2005 to 2019; The research conducted four different case studies to examine the relationship between these variables, with the use of netnography and financial analysis. According to previous studies, a VC firm's reputation, based on the success of past ventures, has a positive correlation with a portfolio company’s post-IPO long-run performance based in the US, but they don’t consider social media as a factor since it was not prevalent during the time of their study, which is something this paper took into account while examining the relationship between VC reputation and a portfolio company’s post-IPO performance. The research found that VC reputation does have a positive correlation with the post-IPO performance of a company; however, the research also found that a VC firm’s social media activity also has a positive relation to the performance of a portfolio company, mostly in the short run, but also lingers temporarily in the post-IPO long run. The implications of these findings suggested that previous assumptions about VC reputation and a portfolio company’s post-IPO performance have slightly, and ultimately, VC firms could leverage their social media presence to boost the portfolio company’s short-run performance, while also giving entrepreneurs an additional factor to consider when finding the VC firm to back their company.

The field of oncology is rapidly growing in search of various therapies for cancer, which calls for further study of cellular processes and molecules. ROS, or reactive oxygen species, are necessary throughout a cell's metabolic processes; high reactivity at high concentrations could threaten cells, possibly allowing for beneficial use against cancers. Conversely, antioxidants regularly neutralize ROS in cells and manage lipid peroxide. Depleting certain antioxidants may lead to ferroptosis, which may also target cancer cells. It has been found that antioxidants can protect healthy cells from ROS treatment while cancer cells suffer damage. However, antioxidants may also diminish the effects of chemotherapy/ROS treatment, increase the risk of cancer, or have no effect at all, so additional work is needed to understand the balance and specific molecules necessary. The metabolic pathways generating ROS, involvement of ROS in cancer, and interplay among antioxidants, ferroptosis, and reactive oxygen species are evaluated in this review.

The COVID-19 pandemic has significantly impacted society, highlighting the need to understand disease dynamics. This paper explored the Susceptible-Infected-Recovered (SIR) model, which categorizes populations to simulate disease spread. By analyzing the transmission (β) and recovery rates (γ), the model provided insights into epidemic behavior. Using COVID-19 data from South Korea (February-April 2020), the study examined the impact of social distancing on the effective reproduction number (Rt). The findings showed that interventions effectively reduced Rt, demonstrating that the SIR model’s predictions align with observed outcomes in managing outbreaks. The paper concluded that even though the SIR model is valuable for showing basic and simple trends for the disease, incorporating Rt and more complex alternative models provides a more comprehensive understanding and enhances response strategies. This approach is crucial for preparing for future pandemics and developing effective public health policies.

We analyzed earnings reports of mega-cap companies to determine rationality of the stock price reactions with behavioral economics. The objective was to evaluate whether behavioral economics was a viable method for interpreting price action. We compiled the top twenty companies by weight in the S&P 500 and examined their price data after an earnings report was released. The data indicated that price reactions were not always rational and concepts such as negativity bias and herding behavior often affected stock prices negatively. Specifically, the companies we analyzed experienced rational reactions to their earnings reports 40% of the time and most price reactions were irrational to some degree. Furthermore, technology companies showcased clear signs of negativity bias and the outcomes of many of their earnings could not be explained with the Efficient Market Hypothesis.

Homelessness in the US is continuing to get worse despite significant support from local, state, and federal governments. It is not only a significant problem for the individual impacted directly but also has a knock-on effect on society at large. This research paper looks at the major causes of homelessness in the state of California and the role of government policies that impact it, focusing on housing supply and welfare support. The research concludes that shortage in housing supply is the major cause of homelessness in California. The situation is exacerbated because of the negative impact of economic and fiscal policies, and limited impact of welfare policies such as CalWORKS. The paper proposes specific solutions for increasing the housing supply, and identifies areas for further research.

Chess is a classic board game with simple rules but requires complex cognitive strategies. Over the years, it has left a huge mark on scientific interest, particularly in cognitive science. This interest led to the conduction of a literature review to summarize the research findings of chess on brain activity, cognition, and clinical applications. Benefiting from the neuroimaging techniques, researchers have identified several more active brain regions known to play an important role in the processing of strategic planning, attention, memory, decision-making, and other cognitive skills that are critical throughout practice of chess. There is solid evidence demonstrating a positive correlation between chess and cognitive abilities. A multitude of studies have been conducted regarding the value of chess intervention as a type of psychotherapy. A chess instructional program improves math skills and overall school performance in children with learning disabilities. Chess therapy is effective in the management of individuals suffering from attention deficit/hyperactivity disorder (ADHD). Chess can also support children with autism by improving certain cognitive and social skills. Playing board games including chess is associated with a lower risk of dementia. Chess intervention can ameliorate the manifestations and quality of life in patients with Alzheimer’s disease. Playing chess can restore, at least partially, the executive functions of patients with schizophrenia. As an add-on, chess-based cognitive remediation therapy helps improve cognitive recovery from substance abuse during the initial abstinence period. Altogether, chess has a promising role in the management of neurodevelopmental and mental health disorders.

Deforestation not only destroys habitats for many plant and animal species but also releases billions of tons of carbon dioxide annually into the atmosphere. In order to meet our growing demand for timber without worsening deforestation, one solution that has received increasing acceptance over the past two decades has been fast growing trees, which produce timber and sequester carbon faster than most trees. This paper focuses on South-East Asia, which is home to about 15% of the world’s tropical forests and a major timber supplying region. The two most commonly cultivated fast growing trees here are eucalyptus and acacia. However, because both are exotic to most of South-East Asia, as well as to the majority of regions where they are grown for timber, they have invasively disrupted local ecologies.  The damage includes excessive water consumption, suppression of local ground vegetation and lower plant and animal biodiversity. Hence, this paper examined a potential native alternative, the jabon species (Anthocepalus cadamba), and compared its performance as timber producer and carbon capturer with other fast growing trees.  From existing research, the dimensions of three different species at various ages from planting to harvest were obtained, from which the above-ground biomass (a measure of timber produced) and carbon weight were calculated.  This paper found that compared to eucalyptus, jabon was superior in both metrics by the time of its first harvest, while compared to acacia, jabon produced more timber but captured less carbon.  These findings suggest that jabon is a very deserving candidate for sustainable timber in South-East Asia, with the added benefit that it is indigenous and non-invasive.  Two areas of further study are exploring factors other than species that affect productivity and carbon capture, and extending the study to harvests beyond the first.

Chronic Obstructive Pulmonary Disease (COPD) represented a complex respiratory disorder influenced by a combination of genetic and environmental factors. This review highlighted the pivotal role of DNA methylation in the pathogenesis and progression of Chronic Obstructive Pulmonary Disease. By examining the function of DNA methylation, particularly hypermethylation, the authors identified key targets genes involved in lung maturation, inflammation, and oxidative stress, which contributed to the worsening of Chronic Obstructive Pulmonary Disease. The review also explored the significance of DNA methylation in the biomarker field, environmental factors, and treatment methods. Through a com- prehensive analysis, the authors elucidated the impact of hypermethylation on genes such as Interleukin-1 Beta, which was associated with inflammatory processes in Chronic Obstructive Pulmonary Disease lungs. Furthermore, the review discussed emerging therapeutic procedures, including epigenome editing through CRISPR technology and DNA methyltransferase inhibitors, demonstrating their potential in personalized Chronic Obstructive Pulmonary Disease management. The authors proposed that the integration of these novel approaches with conventional pharmacological interventions might offer a more comprehensive treatment plan tailored to each patient’s specific molecular characteristics.

This research aimed to investigate the daily oscillations of pivotal atmospheric parameters—temperature, humidity, and carbon dioxide (CO2) levels. The investigation is significant in comprehending Earth's climate dynamics and their repercussions on environmental processes. This study amalgamated primary data from an experiment with secondary research from diverse sources, employing statistical and analytical techniques to dissect these patterns. The findings revealed the intricate interplay of natural and anthropogenic influences on diurnal atmospheric variations, underscoring the imperative of sustained vigilance and research in this domain. Potential applications of these findings include improvements in weather forecasting, climate modeling, and environmental policy formulation.

Solar power, a promising green technology, is known to have a decrease in its efficiency as temperature increases. While solutions such as nanofluids offer the potential to cool the panels, their practical implementation has challenges. This study aims to analyze the cooling performance of a Titanium Dioxide (TiO2) and Zinc Oxide (ZnO) based sunscreen on photovoltaic panels. The experimental set-up consists of polycrystalline solar panels, water, a DIY sunscreen kit including TiO2 and ZnO, a heat fan, a spray bottle, an Arduino voltage sensor and a current sensor. Sunscreen and water were sprayed 10 to 30 times on the backside or frontside of the photovoltaic panel that is 55℃. Statistical analysis was done using SPSS. A temperature drop ranging from 3.5-11℃ was observed as the fluid was sprayed. Spraying on the back side rather than the front side, an increase in the rate of spraying, and utilizing sunscreen instead of water lead to larger temperature drops. Power and temperature showed significant correlations with a Pearson‘s R coefficient of -0.71 (p<0.001). Linear regression showed a R2 value of 0.505 (p<0.001), with an equation of [Power] =－0.338[Temp] + 44.561. Overall, TiO2-ZnO-based sunscreen showed effectiveness on the cooling performance of photovoltaic panels. For further applications, verification of these results and an outdoor study would be needed.

Attention deficit and hyperactivity disorder (ADHD) is a mental disorder that is characterized by obvious difficulty in concentration, short duration of attention, and hasty action. Traditional diagnosis mainly based on subjective report or questionnaire has the limitation of inconsistency and inaccuracy. Main treatment, pharmacology, is also criticized by irresponsiveness, overuse, side effects, or reluctance from patients. Recently, computerized methods have been developed and applied as diagnostic and treatment tools in various medical fields including psychiatry. This review was to describe computerized methods applying for diagnosis and treatment of ADHD that have been used. For this, articles were selected through extensive search of databases and reviewed with regard to diagnosis using machine learning (ML) and deep learning (DL) with extracted data from structural and functional magnetic resonance image, electroencephalography (EEG), and genetic study as well as treatment by computer games based on EEG feedback or not. Overall, the literatures included in this review stated that ML/DL techniques provided objective and reliable diagnostic tools with improved accuracy and computer games achieved better results in reduction of ADHD related symptoms than other traditional treatment. Computerized methods will be promising strategies to accurately select the patients with ADHD benefitted by treatment and provide the effective treatment method. 

Due to rapid urbanization, noise-related sleep disturbances are particularly prevalent in metropolitan areas. (United Nations Department of Economic and Social Affairs. (n.d.)) sThis study investigates how nocturnal noises in the urbanized city influence sleep quality to provide insights in tackling noise-related sleep disturbances. First, a web-based, cross-sectional survey was conducted with convenience and snowball sampling of 338 residents from five residences in Gongshu District, Hangzhou, China. Later, the objective noise exposure around the periphery of each residence was measured by a decibel meter, the level of nocturnal noise exposure was determined based on the Noise Exposure Questionnaire, and the perceived sleep quality was determined using a combined Pittsburgh Sleep Quality and Insomnia Severity Index questionnaire. Lastly, multiple regression models using stepwise variable selection were fitted to determine associations, adjusting for potential confounders. This study showed a significant negative association between nocturnal noise exposure (measured both objectively via a decibel meter and subjectively through the survey) and sleep quality. In the univariate regression model, participants' perceived level of noise disturbances was significantly negatively associated with perceived sleep quality (β=-0.126, p-value=0.020) in the multiple regression model, participants' perceived level of noise disturbances was significantly negatively associated with perceived sleep quality (β=-0.118, p-value=0.000), adjusted for confounding variables, namely volume of tea intake, time spent on outdoor activities, and other lifestyle factors (cellphone usage, midnight snacks intake) by stepwise method.Volume of tea intake was significantly associated positively with perceived sleep quality (β=0.096 and p-value=0.050). This study provided insights into urban nocturnal noise intervention to ensure qualified sleep. More research is needed to determine how nocturnal noise exposure affects objective sleep quality and threshold noise level.

Previous research has indicated that emotions have an effect on investment behavior. However, this research only encompassed a limited range of emotions. While emotions like fear, anxiety, and anger have been frequently studied for their effects on investor behavior and how they affect the stock market, the effects of other relevant emotions such as sadness, surprise, nostalgia and excitement have not been as extensively studied. Furthermore, only a limited range of investment behaviors have been studied. This paper served as a review for the previous research on emotions and investment behavior, and develops a generalized model following a two dimensioned approach incorporating approach and avoidance motivations as well as appraisal theory. Different emotions significantly affect investor behavior and the stock market and emotions with approach tendencies cause risk prone behavior while avoidance tendencies cause the opposite. Additionally, the more an investor is associated with certainty appraisals the more confident they are that their investment will travel in the intended direction. Through this model, propositions are made linking emotions and investment behavior and stress their nuanced relationship. This paper was formulated through previous literature, previous surveys and experiments as well as established psychological principles. This paper deepened the comprehension of how emotions affect investor behavior and emphasized the essential nature of considering a broad spectrum of emotions when analyzing investment decisions.

Cardiac hypertrophy is a major cause of heart failure and a common symptom of congenital heart disease (CHD). Cardiac hypertrophy has usually been studied through the lens of coding genes, but a promising new field of research has emerged regarding the critical role long non-coding RNA (lncRNA) plays with cardiac hypertrophy. Utilizing next-generation sequencing (NGS), scientists have been able to uncover some of these lncRNAs, such as Chaer, Chast, H19, Lipcar, and Hotair, as well as their newly-established association with cardiac hypertrophy. These lncRNAs have potential for use as epigenetic regulators, biomarkers, or even sponges for microRNAs. With 68.4% of non- syndromic cases of cardiac hypertrophy associated with various lncRNAs, understanding the role they play could transform the landscape of cardiac treatments in acute and chronic illnesses with new treatments with lncRNAs, discovered with NGS, pushing forward the front in the fight against heart failure. Epigenetic regulators can either inhibit or promote cardiac hypertrophy. For example, Mhrt and H19 inhibit hypertrophy while Chaer and Chast promote hypertrophy. Targeting specific lncRNAs for upregulation or downregulation could provide insights into developing new treatments for cardiac hypertrophy. However, effective delivery mechanisms for lncRNAs must be established and human trials must be undergone before lncRNA therapies are considered for clinical use.

This literature review explored the emergence and evolution of hybrid cultures from the rise of the Renaissance and the Scientific Revolution in the 16th century. It examined how European pursuits of exploration and trade, beginning with Portugal's maritime route to India and Spain's exploration of the "New World," propelled Europe into an era of colonization. This review highlighted the economic partnerships and religious opportunities that facilitated cultural exchanges, emphasizing the agency indigenous peoples maintained despite colonial pressures. By analyzing the profound impact of colonialism, including forced conversions, cultural destruction, and resistance, the review revealed how indigenous peoples navigated and negotiated their roles, contributing to the formation of hybrid cultures. These interactions created opportunities for social and political advancement, leaving enduring impacts on language, culture, and music. The study also suggested future research into the religious aspects of hybrid cultures in North America, focusing on conversion efforts and their influence on local hybrid cultures.

This research paper investigated the sources and dispersion of PM2.5 particulate matter in New York City's Morrisania neighborhood from January 2018 to August 2023, using Conditional Probability Function (CPF) analysis alongside detailed meteorological data. The study identified significant local PM2.5 sources, notably transportation emissions from nearby highways and industrial activities from New Jersey, and examined how varying wind patterns affect pollutant dispersion. Key findings revealed that winds from the northwest promoted efficient PM2.5 dispersion, resulting in lower concentration levels, whereas slower winds from the south, southwest, and west led to higher concentrations due to reduced dispersion capabilities. This paper also evaluated the limitations of CPF analysis in distinguishing between local and distant pollution sources, suggesting the incorporation of advanced methodologies like Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) models for future research. The results had important implications for air quality management in urban settings, providing a framework for targeted interventions to reduce PM2.5 pollution and improve public health outcomes. The insights gained underscored the necessity of integrating more detailed local data and modeling techniques to enhance the understanding of air pollution dynamics and inform effective policy-making. 

The venture capital industry plays a pivotal role in fostering innovation and driving economic growth by providing required funding to start-ups with long-term growth potential. In this high-risk environment, the choice of pricing mechanisms (whether posted price or auction) for start-up companies can significantly impact investment decisions, allocation of resources, efficiency, and finally success of the ventures. This research paper delves into an in-depth comparison of a first-price sealed-bid auction against a posted price system within the context of the venture capital industry. The goal is to compare their relative advantages, limitations, and overall implications for venture capitalists and entrepreneurs by starting with deriving optimal strategies in a standard posted price and auction model with bid preparation costs. The second part of this paper will focus on maximising the expected utility and the total capital raised by the start-up, while comparing optimal strategies within each model. The analysis shows a negative relationship between bid preparation costs and the total revenue generated, which substantiates the existence of miscoordination due to fear of wasted efforts and predicts the effect of priority on efficiency. However, the extent to which each factor impacts the equilibrium varies greatly with changes in the pricing mechanism.

Minority coalitions have ubiquitous presence across the globe, from the United States to regions in Africa. These ethnic enclaves serve a purpose beyond merely preserving their own culture; they also function as protective shields, almost a sanctuary, against local and regional pressures. As minorities in their respective regions, minority coalition communities have faced significant discrimination, sometimes even targeted by state policies focusing on weakening their influence. Despite these government efforts, minority coalitions have adeptly resisted amalgamation, eventually compelling their host countries to recognise and celebrate their cultural distinctiveness, often transforming these areas into popular tourist destinations. This paper aimed to delve deeper into the underlying reasons among social, political, and economic factors for the formation and development of such ethnic coalitions, arguing that the economic factor is the most important factor out of all. In doing so, out of many minority coalitions, the paper argues that Chinatown serves as an example and a representative that will be mainly discussed when pertaining to minority groups. It explored the intrinsic need for these communities to establish their own communities in response to the challenging social, political, and economic reasons, resulting in the creation of minority coalitions. Especially among these factors, economic reasons were identified to be the driving factor for such dynamics within the minorities. By shifting the discourse away from conventional political and social analyses toward economical factors, the results of the study are anticipated to further enhance our comprehension of social cohesion and identity formation within chinatowns. Furthermore, the results can be further utilised for a better understanding of how those economic factors interact with different social and political factors to shape the dynamics of ethnic enclaves, aiding in policy decisions for immigrating populations.

Music therapy is commonly used to improve people's mental and physical health. However, the role social media plays in this therapeutic practice has not yet been thoroughly explored. One study looked at the benefits of music therapy in 40 Australian adolescents. Another study found how music affects the perception of agency in 44 adolescents. Though these papers reveal the positive effects of music, they do not consider an important factor in their research– how much their subjects are exposed to media. This study seeks to explain research questions such as the extent to which social media usage influences the efficacy of music therapy interventions and how individuals' interaction with social media platforms shapes these musical experiences. To achieve these objectives, the author conducted a comprehensive review of existing literature, analyzing articles and journals related to music therapy and social media. This will provide an understanding of the synergies between music therapy and social media in aiding mental and physical well-being. Our study aims to explore the impact of social media exposure on the efficacy of music therapy interventions, illuminating the interplay between digital environments and therapeutic outcomes to inform and enrich practices tailored to the needs of the digital age.

In this paper, the primary objective is to create and validate a predictive model using neural networks to forecast high school students' likelihood of pursuing higher education to contribute to the body of knowledge in educational planning and policymaking. The foundation of the analysis rests upon a comprehensive dataset, encompassing diverse information about 1000 students. Pertinent factors considered include the nature of the educational institution attended, the institutional quality, gender demographics, and other relevant parameters. This study leverages the robust capabilities of Keras, a widely acclaimed open-source library nested within the TensorFlow framework. The modeling approach adopts a neural network architecture, featuring a sigmoid activation function in the output layer. To mitigate the potential risk of overfitting, this study integrates regularization techniques into the model construction process. The dataset undergoes a partitioning into a training dataset, constituting 75% of the samples, and a validation dataset, comprising the remaining 25%. The training process involves the application of the neural network on the training set, facilitating the refinement of the model's parameters. Subsequently, the validation set is employed to assess the model's generalization performance, affirming its efficacy in extrapolating insights to novel examples. This research not only showcases the utilization of cutting-edge machine learning tools but also emphasizes the significance of thoughtful data preprocessing and model validation methodologies. The results gleaned from this study contribute valuable insights into the predictive factors influencing a student's likelihood of pursuing higher education, thereby fostering a nuanced understanding of educational trajectories.

Classified as hallucinogens, psilocybin is a psychedelic drug that affects our psychological processing. Recent studies highlight the benefits of controlled doses utilized as a medication for depression and other mental health illnesses to exert the positives of synthetic medicines without the side effects that often occur. By regulating mood and through various brain mechanisms the drug modifies, the personalities of individuals can improve. Of the Big Five personalities, patients displayed an increase in extraversion and openness levels- possibly through serotonin uptake. Based on varying dosages and environments upon experiment, individuals present a diversified set of results to analyze. Despite evidence presenting support for the usage of psilocybin, research on this subject is limited as studies are heavily regulated and not often approved for further investigation. Additional analysis should be organized before deciding on the legality of this hallucinogen; in the future, when additional evidence becomes available, public opinion on psychedelic drugs may change depending on scientific authentication of advantages and disadvantages regarding the effects of psilocybin. It’s vital to foster an environment where research can be further conducted to fully understand the therapeutic potential and risk associated with use.

Evolution in technology has shown a noticeable gap between Gen X and Gen Z. Both generations manifest excessive screen time. To understand the market research, the focus was on surveys, focus groups, interviews, observations and field trials. The majority of both generations use cell phones over any other electronics. Cell phones are portable, which indicates the immoderate times of screen time. Screen time has proven to damage both mental and physical health of an individual. After reviewing the response survey from both generations, it proved they are entirely dependent on electronics, making this trend irreversible. There has to be a solution to avoid using gadgets, and increase the amount of face-to-face interactions between both generations.

Since there is no cure for Parkinson’s it’s essential detecting the disease early and accurately. However, it’s extremely challenging because brain MRIs in the early stages looks normal to a human eye. Deep learning convolution neural network models using custom and transfer learning approaches were used to detect and classify Parkinson’s disease. MRI images were collected from Alzheimer's Disease Neuroimaging Initiative datasets for both Parkinson’s and control-normal classes. VGG16, ResNet-50 and DenseNet-169 base models were used for the transfer learning convolution neural network study. Transfer learning models VGG16, ResNet-50 and DenseNet-169 achieved score for accuracy and precision of (68, 77), (63, 64) and (81, 87) respectively but their performance was impacted by fewer number of datasets and class imbalances. Siamese neural networks which work well with fewer number of datasets was used in this study. For Siamese neural network, transfer learning approach was used via ResNet-50 for Parkinson’s disease classification. Siamese neural network model achieved on outstanding score for accuracy and precision of (99,99). Siamese neural network approach also detected and extracted the region of interest as the corpus callosum region.

Gaming disorder, which is often associated with an addiction to video games, is characterized by an increasing prioritization of gaming. This issue affects people of all demographics and inhibits one from performing daily activities, leading to increased anxiety and distress. This study outlined the multitude of factors associated with anxiety levels among video game players and a range of demographic and gaming habit variables. By analyzing demographic factors and gaming habits, this research uncovered potential predictors of anxiety levels among those who play video games in order to contribute to a better understanding of general anxiety among certain demographics of video gamers. This paper studied what variables are associated with game players’ anxiety levels. The dataset used in this study consists of 6 groups of variables collected from 13464 users, including Generalized Anxiety Disorder, satisfaction with life scale, social phobia inventory, single-item narcissism scale, gaming habits, and demographics. Correlation analysis and Chi-square tests were conducted to study the association between these variables with general anxiety. This study suggests that younger people as well as females are more prone to developing Generalized Anxiety Disorder. In addition, players who spend more hours playing video games and those who do not have an education degree tend to be more prone to developing Generalized Anxiety Disorder.

Large-scale scientific instruments generate increasingly large amounts for data analysis. Data compression can reduce the data cost by reducing the size of data saved on disks. This paper explores an extrapolation-based compression algorithm commonly used by many state-of-the-art compressors, investigating its performance. The paper develops two extrapolation schemes in C++, linear and piecewise extrapolation, and evaluates their performance on three real datasets. The key finding of this paper is that extrapolation effectively exposes data redundancy, which means data can be better compressed further. However, the best extrapolation scheme to use depends on data characteristics and needs to be chosen carefully to achieve the best performance.

The complexity of the tumor system presents a formidable challenge in developing an optimal cancer treatment. Mathematical modeling and computer simulation are increasingly being utilized to predict tumor growth and investigate the efficacy of cancer therapeutic strategies, such as chemotherapy, in suppressing its growth. Along with laboratory experiments, this approach has been utilized to develop an optimal treatment for individual patients. This paper presents a system of ordinary differential equations consisting of the Gompertz and exponential decay models that describe the dynamics of tumor growth and chemotherapy drug concentration. A numerical method was utilized to simulate and solve the model and was further used to examine the effectiveness of various treatment schedules and dosages in suppressing overall tumor growth. The results suggested that frequent smaller dosages of the chemotherapy drug are more efficient than less frequent larger dosages. This study illustrates the use of mathematical model as a predictive tool to help guide laboratory experiments in developing an optimal cancer treatment.

This review paper focuses on indigenous tribes who inhabited the northeastern part of United States for over ten thousand years prior to European contact in 1620. Our research, which involved consulting various databases such as government and museum sites, archaeological journals, research papers, articles and books on northeast indigenous tribes, aimed to gain an understanding of how these tribes managed to survive for centuries. These tribes acquired knowledge and skills that allowed them to adapt to the changing climate and environment of the northeastern United States which had been evolving significantly since the post ice-age era, beginning approximately 11,700 years ago. These tribes utilized natural resources including native plants and herbs for medicine and developed unique dwelling techniques in construction of their houses in order to survive harsh winters. They used natural flora and fauna such as wild berries, animals and aquatic life for food and other necessities. They developed farming and gardening methods which supported soil conservation and crop interdependencies. These tribes preserved and stored food for winter by utilization of smoking and drying techniques. This research helps to conclude that the indigenous tribes of the Northeast (Algonquins and Iroquois) were advanced societies. They were self-sufficient, and had an impressive amount of understanding about their surrounding natural environment. The age-old techniques and practices used by these tribes can further benefit modern societies including the development of effective therapeutics and support for climate initiatives.

Since 1981, HIV/AIDS has existed as a prominent global epidemic. Throughout the years, research on the disease has greatly improved and expanded as technology has advanced. Because a latent reservoir of dormant viruses persists in the T cells of HIV patients, eliminating these infected cells is a primary goal and challenge for research on the virus. Over the past 40 years, there has been significant research on a variety of potential solutions, yet a functional cure remains elusive. Current antiretroviral treatments can effectively ensure the inactivation of the latent reservoir, but patients must take these medications for life. Therefore, researchers are actively searching for a cure that can either eliminate dormant viruses or assist the immune system in eradicating them once treatment is stopped. The most promising methods include the “kick and kill” strategy to reveal the latent reservoir and remove it and gene editing methods to create immunity to the virus in patients. However, both of these techniques require further study to improve their effectiveness. Based on an evaluation of the current research, a combination of approaches may lead to more effective results. This review also covered efforts to create HIV/AIDS vaccines and improve treatment methods. Due to ethical concerns around testing experimental cures in human patients, the scope of HIV/AIDS research has been limited. Many trials contain relatively small sample sizes and primarily include Caucasian men as research subjects. A resurgence in research efforts and greater inclusion of women and people of color in research may provide greater insights in finding effective solutions.

This study examined the significance and mechanisms of the roles of the determinant factors play in gaining sustainable competitive advantage. The factors include supply chain resilience, performance, and awareness of potential disruptions. The investigation was conducted within manufacturers in the US in the context of their supply chain operations under disruptions. Employing a linear regression on the four factors, this empirical examination shows that supply chain resilience, performance, and awareness of potential disruptions exert positive effects on the improvement of sustainable competitive advantage. Additionally, this study investigated the role of market volatility, which plays a negative role in building sustainable competitive advantage and the role is marginal. The findings of this study offer practical implications to supply chain managers. Limitations of this study suggest future research directions. First, the regression analysis was based on a dataset with small sample size, which might constrain the generalizability of the findings of the investigation. Further research might include a substantial sample size to better represent the manufacturers in the US. Second, inventory management can impose disruptions on supply chain operations and generate ripple effects in a supply chain. However, it is not considered in the study. Future research is expected to assess its role in building sustainable competitive advantage.

Alzheimer's Disease is a degenerative disorder of the brain that results in the deterioration of memory, language, and behavior. The accumulation of a protein called amyloid beta is responsible for the development of Alzheimer's Disease. This protein forms plaques in the brain, obstructing communication between neurons and playing a crucial role in the progression of the disease. Potential treatments aiming to reduce the presence of beta amyloid plaques include immunotherapy (both active and passive), enzymes that degrade beta amyloid, and inhibitors of beta secretase. Unfortunately, effective treatments for Alzheimer's Disease are currently unavailable due to the severe side effects and limited effectiveness of existing options. As Alzheimer's Disease is influenced by various factors including but not limited to amyloid beta proteins, a comprehensive study that includes clinical trials becomes vital in finding an effective treatment. Clinical trial data suggests that beta amyloid degrading enzymes show the most promise as a future treatment, as they have successfully reduced amyloid beta plaque levels in the brain with minimal side effects compared to the other treatments. This review aims to provide a basic understanding of Alzheimer's Disease by exploring the interactions between neurons, amyloid beta proteins, and other internal factors that contribute to the disease's development. Additionally, it highlights three potential treatments and their approaches to preventing or reducing amyloid beta proteins, supported by relevant clinical trial data. Finally, the review discusses the side effects and limitations of current treatment options while considering the potential of becoming viable future treatments.

Understanding the various stages of sleep is crucial in diagnosing and treating sleep disorders like insomnia and sleep apnea. The current methods used to determine sleep stages involve using a polysomnogram (PSG), a sleep diagnostic tool in healthcare, to examine a patient’s activity throughout their sleep. In a polysomnogram study, an electroencephalogram (EEG) records the brain's electrical activity to determine signal amplitudes at various frequency bands (e.g., alpha, beta, theta, delta). Sleep technicians then examine electroencephalogram data to determine each sleep state during the patient’s sleep. However, this process is labor-intensive. This paper investigated spectral and temporal features of a group of patients' electroencephalogram signals, such as the amount of frequency present and its changes and behaviors over time. It is revealed through the paper’s results that analyzing these features with a classifier would provide experts with a more efficient way to score sleep. Distinguishing corresponding sleep stages from wake/sleep characteristics would give physicians a more accurate and accessible scoring system than existing human performance sleep scoring methods. This research anticipates a transformation in healthcare by increasing productivity in sleep scoring, allowing patients with severe sleep disorders to be diagnosed and treated faster. By creating a classification system that categorizes each sleep stage, this paper provided a way for physicians to move from tedious human sleep scoring to automatic sleep scoring, improving patients’ sleep-based health issues.

Eighty nine children were recruited for the study during the child’s routine asthma clinic visit by their provider. Everyone between 7 and 13 years of age were eligible. Those children and parents who agreed to participate were introduced to the study team waiting in a conference room where the study occurred. Pulmonary Function Tests were collected to provide the biological function of the child’s lungs. In return, the child would receive a $10 Amazon gift card. The child completed the first activity by replicating the Asthma Control Test and the second activity by filling out the new survey tool, the Child Asthma Severity Tool. Results showed that the Asthma Control Test is significantly correlated with aspects of the Pulmonary Function Test but more importantly, it showed that the Child Asthma Severity Tool is significantly correlated with the Pulmonary Function Test.

Cyberbullying on social media platforms recently became a serious social issue with significant psychological impacts on individuals. This study focused on cyberbullying detection on multiple social media platforms. A related dataset was acquired from Hosseinmardi et al., and the research was conducted based on this dataset. The methodology combined both deep learning and traditional machine learning models. Experiments were conducted with various machine learning algorithms, including logistic regression, random forest, support vector machine, and Naive Bayes classifier. The DistilBERT transformer model was tested alongside these machine learning models. In the end, the Naive Bayes classifier outperformed the other models, including the transformer model, with an accuracy of 95.83%. The results indicated that while complex deep learning models like DistilBERT were often superior for natural language processing tasks, probabilistic models like the Naive Bayes classifier could sometimes yield better results. The insights from this study contributed to the broader understanding of cyberbullying detection and paved the way for future research to integrate multimodal data, explore real-time detection systems, and more.

This research paper explores the multifaceted economic impact of chess, spanning its historical foundations, cognitive and educational contributions, and its evolution into a digital and commercial powerhouse. Tracing the game's origins to ancient India, it highlights how chess catalyzed cognitive growth, becoming a solid investment in education. The study meticulously analyzes the economic stimulation generated by chess tournaments, from local events to the prestigious World Chess Championship. The digital disruption is examined through the lens of online platforms, revealing a thriving digital chess economy. The paper also delves into the commercialization of chess through merchandise and intellectual property, citing examples like "The Queen's Gambit." The material and methods section outlines a comprehensive data collection and analysis process, with statistical techniques applied to discern trends. The results present a decade-long upward trajectory in the global chess market, reaching 40.5 million enthusiasts in 2022. The discussion contextualizes these findings, comparing them with other research and emphasizing the consistent growth in chess's influence. The paper concludes by highlighting the implications of chess's economic ascent in a rapidly evolving global economy, emphasizing its role as a valuable educational tool and strategic player shaping industries and minds.

Previous studies have concluded that as individuals get older, they become more prone to experiencing loneliness due to significant economical, social, and cognitive changes in their lives. For instance, older adults are more likely to experience more loss in their social network and become reluctant to initiate new networks. Social isolation and loneliness are two major factors damaging elderly adults’ health. With the population of elders aged 60 years and older increasing, it is important to understand the root cause of detrimental senior health outcomes in order to minimize its effects. Despite this, seniors’ health is often neglected compared to other age groups. Due to lack of sufficient and cohesive data, there is a need for a comprehensive review, to gain a better understanding. The objective of this paper is to analyze the correlation between social isolation of elders and various health issues. Loneliness and social deprivation of elders significantly increased the likelihood of psychological, cognitive, and physical health issues, such as depression, dementia, and cardiovascular diseases.

In the current swimming environment, swimmers are placed in lanes based on the timings they had swum in the preliminaries, with the fastest swimmer traditionally being in the middle lanes (lane 4). It is currently known that there is no physical disadvantage to being in the outside lanes, but the psychological effect is not known. This study will be determining if lane placement does play an effect on collegiate-level swimmers in the United States. This study aims to find deficiencies in the National Collegiate Athletic Association’s (NCAA) running of the national swimming championships and remedy them to make swimming a more equitable sport where every athlete is given an equal chance of performing. It was found that no statistical results could be made with a p-value of 0.21, but other conclusions could impact the NCAA and the audience: maintaining audience expectations for a swimmer’s performance and contributing to the scholarly conversation surrounding swimming and the factors that impact swimmers.