bluuebunny/crossref_metadata_2025 huggingface dataset:
A Metanalysis of Parkinson's Disease
Authors: Wang Sarah , Avery Emily
Published: August 2024

Parkinson's disease is one of the most prominent and devastating neurodegenerative disorders worldwide. However, each PD patient has a unique disease course due to individual variations in the rate of cognitive and psychomotor decline. In this review article, we will analyze factors influencing such individual variation and the amount of concrete information we currently have on PD. We will focus particularly on genetic factors, social/socioeconomic factors, and medical comorbidities using existing data and information gathered from previous experiments. We will discuss the implications such factors should have on treatment options and disease management and the effects the COVID-19 pandemic had on PD patients.
The Price of Calm: How Socioeconomic Stressors Shape High School Student
Authors: Walia-Peters Madison , Cavanaugh Chloe
Published: August 2024

This research article examined the correlation between socioeconomic situation (SES) and the mental and emotional well-being of high school students during the 2022-2023 academic year. The study aimed to gain a comprehensive understanding of how SES-related stressors impact students' well-being. The research covered 49 students, collecting data through a 31-question survey that included demographic information and well-being measures modeled after established mental health questionnaires. The findings demonstrated that SES has an influence on the mental and emotional well-being of high school students, with lower SES backgrounds being associated with increased stressors that affect calmness, stress levels, and overall well-being. Higher income levels were significantly linked to higher calmness levels, while longer working hours during the academic year were significantly connected to higher self-reported stress levels. However, there were no statistically significant relationships between income and self-reported sadness and anxiety. The study also highlighted the importance of access to healthcare, as students with health insurance and therapy services reported slightly lower stress levels. While this research provides valuable insights, further studies are needed to deepen understanding and inform targeted interventions for student support.
The Feasibility of Full Sustainability in the Fashion Industry
Authors: Qu Yue Ge
Published: August 2024

The fashion industry exerts substantial environmental impact with its significant carbon footprint, extensive water pollution, and microplastic contamination. This study investigates the feasibility of sustainable fashion and analyzes the main roadblocks to achieving it, including greenwashing, consumer overconsumption, and waste during production and recycling. A survey study was done to garner information regarding the public sentiment toward sustainable fashion, which showed an overwhelming response of uncertainty towards the prospect of sustainability. The results of this study may be used to advise the practice of major fashion corporations in improving their business models and seeking more sustainable alternatives for profit.
Securing Digital Lives: Personalized Measures for Comprehensive Data Protection for Home Networks
Authors: Vora Rishi , Gochhayat Sarada Prasad , Torremocha Virgel , Kethar Jothsna
Published: August 2024

This research paper addresses the increasing cyber threats targeting home networks and the lack of comprehensive security solutions tailored for consumers. It proposes a comprehensive solution that addresses data protection, threat monitoring, and impact mitigation, which could be automated through AI and Federated Learning (FL) to simplify security for non-experts. The paper conducts a literature review and qualitative analysis to examine cyber threats and security research, proposing an architecture that utilizes FL across home devices. It discusses challenges such as handling biases and diverse devices, limitations of interoperability, and hardware requirements. The paper emphasizes the urgent need for automated and personalized cybersecurity tailored for consumers to address the escalating threats to home networks, arguing that FL has the potential to enable this while addressing privacy concerns associated with centralized AI.
The Effects of Energy Commodities on CPI
Authors: Chen Daniel , Yang Richard
Published: August 2024

From 2021-2023, the US saw a period of unparalleled inflation. Prompted by the debate on the role of energy prices, this paper investigates the role of rising energy commodities in this inflationary trend. Focusing on the Consumer Price Index (CPI-U), we explore the direct and indirect effects of energy commodity price changes. Despite the sector's 3-5% CPI weight, energy commodities exhibit significant volatility, contributing to 30% of monthly inflation fluctuations on average. The study identifies Gasoline and Fuel oil, both crude oil derivatives, as key components. Additionally, we assess the secondary pass-through effects, mainly through the transportation and industrial sectors. This research provides insights into the substantial impact of energy commodities on inflation and encourages further exploration on this topic, which can enhance economic forecasts and possibly monetary policy forecasts.
Environmental and Hereditary Effects on Intelligence and Learning Capabilities
Authors: Melgen Aliah , Lopez-Figueroa Johnny
Published: August 2024

A person's intelligence is defined by their different cognitive abilities, such as thinking fast, solving problems, developing questions, understanding, and using critical thinking; these abilities are far more important than general intelligence acquired in a school system. The environment may affect a person, such as having no support from one's parents to study or do well in school and develop cognitive skills. Cognitive skills can be improved, such as attention span, which may improve with consistent practice. Everyone learns differently and requires different modules of learning; people who have high IQs and do well in school tend to be because they fit into the learning module that schools provide. This research will talk about adaptive intelligence and how it is essential to solve problems of the moment because a person with this intelligence has the necessary tools to adapt their skills to any situation. It will also cover the understanding of human abilities, the factors that may affect them, the difference between households, and the effect this can have on a person, such as the amount of support and motivation given to a person to learn and understand. Furthermore, this investigation delineates the importance of education and how it is vital in helping people develop their cognitive skills and improve them over time.
Developing a Novel Deep Learning Model for Pediatric Chest Radiograph Analysis
Authors: Choi Heeyoon , Rose Gregory
Published: August 2024

Pediatric thoracic diseases pose significant challenges to healthcare systems worldwide, particularly in regions with limited access to specialized medical resources. Lower respiratory infections, in particular, contribute significantly to childhood morbidity and mortality. This research aims to address the diagnostic challenges associated with pediatric thoracic diseases by harnessing the power of artificial intelligence. The study utilizes the PediCXR dataset, composed of pediatric chest radiographs, and employs a Faster R-CNN model pretrained on ImageNet data for lesion detection and classification. The model is trained to identify abnormal lesions indicative of various thoracic diseases, with a focus on achieving high accuracy and minimizing loss. Experimental results demonstrate the model's capability to achieve an accuracy exceeding 90% and a loss under 0.1, meeting predefined objectives.
On the Effectiveness of Emergency Reduction Measures for Ultrafine Dust Concentration in Seoul, Korea
Authors: Park Sophia , Joo Youngchan
Published: August 2024

Since 2018, emergency reduction measures (ERMs) have been implemented by the Seoul Metropolitan Government to address air quality in Seoul, Republic of Korea. ERMs are activated when the concentration of ultrafine dust, particulate matter (PM) with an aerodynamic diameter of less than 2.5μm (PM 2.5), exceeds a certain level. In this study, using the daily time series data from 1 January 2018 to 31 June 2023, the effectiveness of ERMs is empirically analyzed by specifying multiple regression models in which PM 2.5 is explained not only by the days of ERMs but also by many other important factors. The empirical analysis shows that (i) PM 2.5 is influenced by its own past values up to three days; (ii) PM 2.5 is negatively influenced by both rainfall and wind speed, but positively related to temperature, humidity and yellow dust. The effect of PM 2.5 from the neighborhood (Dailan, China) is significantly positive; (iii) the effect of vehicle traffic volume and coal thermal power generation on PM 2.5 is not statistically significant; (iv) the effect of ERMs is not very strong when many important control variables are included in the regression equation. In conclusion, the ERMs do not play an effective role in reducing PM 2.5 over the whole period. However, it can be seen that the effectiveness of the ERMs has been increasing in recent years.
The Portrayal of Women in Media
Authors: Morales Rodríguez Gabriela , Lopez-Figueroa Johnny
Published: August 2024

This study looks at the history of women's depiction in media, particularly in the early 2000s, and discovers a substantial shift in the image of young female characters. Historically, media narratives have constrained women to traditional roles, depicting them as caring figures or hostile antagonists. This shift occurred in the early 2000s when the women interested in the male lead were tomboys or boyish, not the stereotypical image of femininity. Femininity was vilified, causing young girls to separate themselves from their femininity and try to appeal to men by acting like tomboys. This investigation analyzes the portrayal, erasure, objectification, and vilification of different kinds of women with different social, political, and economic backgrounds.
A Documentary Analysis on the Impact of Untested Rape Kits on Victims' Psychological Health
Authors: Haynes Madeline , Lopez-Figueroa Johnny
Published: August 2024

The rape kit backlog impacts both victims and society at large. This paper delves into the psychological impacts on victims. Due to the justice system's failure to adequately address sexual assault, many people have a natural distrust of the system and its capabilities. An abundance of unprocessed kits have been discovered across the US; however, in this paper, Missouri and Minnesota are the primary examples. There has been an abundance of papers discussing the legal aspects of the backlog; however, victims have been left voiceless, and little academic research has discussed the psychological impacts. By focusing on the way that the rape kit backlog affects the victims, their voices can be included in the conversation about this issue. Furthermore, future research must include examining this issue from the victim's perspective rather than only from the legal perspective. By informing people of this issue and giving a voice to the victims, more funding could be received to test rape kits.
Brushstrokes of Change: Hieronymus Bosch and Rachel Gadsden's Evolving Portrayals of Disability
Authors: Choi Heyon
Published: August 2024

There has been a notable lack of representation of disability in Western art history. Where disabilities are displayed, the artists’ intentions were often to convey moral corruption, religious infidelity, and the faults of society. Other times, the representations functioned scientifically as medical records. In fact, one of the most anatomically accurate representations of physical disability is credited to the early modern artist Hieronymous Bosch, who was known for his unfiltered commentary on the flaws of human society. Contrary to his typical surreal, vibrant, and sarcastic style, Cripples and Beggars (after 1570), a naturalistic and observational piece, offers a rare lens into how disability was portrayed during the early modern period.1 In contrast, Rachel Gadsden—a contemporary, disabled artist—foregrounds her identity as a disabled artist in her work Ubuntu (2012) and utilizes an expressive, abstract style to convey the fluidity of the disabled human form. Cripples and Beggars and Ubuntu, separated by centuries, provide a captivating shared subject for comparative analysis that transcends temporal boundaries. Cripples and Beggars provides an early explicit and moralizing depiction of physical disability as an isolating scourge while Rachel Gadsden focuses on abstract, yet recognizable human bodies in optimistic motion and community. Analysis of the varying style, medium, and intention reveals the evolved reception and, thus, depiction of disability. This transformation, from Hieronymous Bosch's Cripples and Beggars to Rachel Gadsden's Ubuntu, signifies a shift from viewing disability as a symbol of moral corruption and societal decay to an expression of identity, resilience, and community.
Monet’s Unified View: The Fusion of Eastern and Western Cultures
Authors: Yoon Monica
Published: August 2024

When Japan opened its borders in the 1850s, citizens, especially those from France, were awed by the different styles of art the Japanese had compared to the West. As this interest in Japanese culture grew, more and more people collected Japanese prints, books, and other goods. Many artists took inspiration from the Japanese art style, such as the use of bright colors, unusual compositions, and flat, unrealistic perspectives. This eventually led to the creation of a new art movement called Japonisme. One major artist within the movement was Claude Monet, who fell in love with Japan’s style of art and admired nature's role within it. Inspiration from the culture can clearly be seen in his Japanese-styled garden at Giverny but also in the painting La Japoinaise (1876), which allowed him to critique fellow French artists who embraced the Japonisme movement for its exoticism. He instead relied on Japanese forms and color to create his own style of art. While most scholars agree that Monet was trying to criticize Japonisme, they mainly focus on identifying elements of Japanese culture in the painting, without fully addressing why and how Monet utilized these stylistic traits. Rather than focusing solely on aspects of Japanese culture in the painting, my paper positions Monet as a critic of those who denigrated Japanese art by analyzing how he unifies Japanese and French culture in La Japonaise, mainly through examination of the Japanese story depicted on the woman’s kimono. Within this painting, Monet, thus, simultaneously critiques the French appropriation of Japanese culture for its exoticism while melding Japanese literary and artistic traditions with Western ones to showcase to the rest of the art world his specific vision of the unification of the two cultures.
Comprehensive Safety Strategies and Innovations for Infrastructure Resilience in Bridges
Authors: Chung Daniel , Klass Steven
Published: August 2024

This study explores the critical elements of ensuring bridge safety, covering design, construction, maintenance, personal protective equipment (PPE), community engagement, and public perception. It draws lessons from historical bridge failures, like the Morandi Bridge collapse, highlighting the necessity for stringent safety protocols in infrastructure development. Innovative solutions, such as heating systems to prevent ice formation and seismic resilient designs, are explored to address maintenance challenges and environmental impacts. In addition, the study also examines the role of public perception, education, and community engagement in bridge safety. It discusses the positive and negative aspects of safety measures like nets and Fall Protection Systems (FPS) and evaluates the impact of public education programs on safety awareness. Visual standards, clear signage, and innovations in bridge design, such as smart sensors and self-healing materials, are explored for their contributions to safety and longevity. A comparative analysis of safety standards worldwide, such as AASHTO, BSI, CSA, Austroad, Eurocodes, JRA, TB, IRC, ABNT, and SANS, highlights global efforts to establish consistent safety measures. Historical data and case studies, including the Tacoma Bridge Collapse and the Minneapolis I-35W Collapse, provide insights into safety measures' evolution. In conclusion, there is a need for a comprehensive approach to bridge safety, on the backend by implementing policies and ensuring safety protocols are being met, as well as a good public perception using visual components, like safety nets. Success hinges on collaborative efforts, robust risk reduction, and fostering public confidence in infrastructure development.
Nanotechnology for Neurological Disorders: Application of Nanotechnology in Diagnosing and Treating Neurodegenerative Diseases
Authors: Haswani Aveena , Torremocha Virgel , Lilova Kristina
Published: August 2024

Nanotechnology is applied for diagnosing and treating neurodegenerative diseases such as Parkinson’s and Alzheimer’s presents riveting opportunities. Targeted drug delivery systems that can cross the blood-brain barrier also known as the BBB can be developed, and there can be particular biomarkers that help in enabling early diagnosis and monitoring of diseases. Additionally, nanotechnology offers potential solutions for improving the delivery of therapeutics to the brain for Alzheimer's disease. The impact of Parkinson's and Alzheimer's diseases worldwide, the current issues in treating these diseases, and how nanotechnology can help develop innovative treatments targeting the underlying mechanisms of these diseases for more effective interventions in the future. Nanotechnology uses nanoparticles which are being used increasingly in various medical fields, including drug delivery, and disease diagnosis. The use of nanoparticles in medicine has shown great promise in the treatment of multiple diseases, specifically neurodegenerative disorders, such as Alzheimer's and Parkinson's disease. The unique properties of nanoparticles are their small size but large surface area, making them ideal for targeted drug delivery.
Enhancing Mental Health Services in Schools: A Comprehensive Review and Recommendations
Authors: Choi Caroline
Published: August 2024

Mental health care in educational settings can be pivotal in shaping student academic performance and long-term outcomes. Global mental health policies are examined, exemplified by initiatives like the European Framework for Action on Mental Health. Despite positive steps, challenges such as insufficient data and knowledge hinder effective implementation. In the United States, federal initiatives like the Every Student Succeeds Act and Project AWARE aim to address mental health needs, yet state-level disparities persist. Mindfulness-Based Interventions (MBIs) emerge as promising approaches, demonstrating positive outcomes for individuals without preexisting mental health disorders. Careful intervention planning involving trained facilitators and consistent implementation is crucial. This study highlights research gaps and a knowledge void in mental health strategies, advocating for standardized parameters and shared terminology. It raises concerns about the increasing use of antidepressants, proposing mindfulness-based interventions as a financially feasible alternative. Best practices and frameworks are suggested, advocating for specific, universal terminology, financial sustainability through integration into existing programs, and a multi-modal approach involving the broader community. Collaborative efforts, standardized methods, and ongoing research are necessary to address the mental health epidemic in schools effectively.
Exploring the Interplay Between Religious Music, Emotions, and Ethics in Cultures Worldwide
Authors: Cho Brendan , O'Connor Paul
Published: August 2024

There is no doubt that music significantly impacts people in their daily lives, emotionally and spiritually. This study explores the impact of religious music on people’s well-being, emotions, and ethical values. The paper provides evidence supporting the positive effects of religious music on mental well-being, reducing psychological distress, and enhancing life satisfaction. The literature review showed that within musical and religious activities, individuals have positive associations between certain aspects of musical and religious experiences and specific domains of happiness, such as sociability, satisfaction with life, and well-being that were not seen in correlation with happiness to membership of religion. It also analyzes the role of music in reinforcing morals and ethical standards within religious worship by influencing pro-social behavior and ethical decision-making. Listeners are attracted to the immersive world of religious music in reaction to the songs' symbolic suggestions, which forces them to reflect on moral quandaries and solidify their ethical views. Religious music serves as a tool to encourage a more robust emotional engagement, resulting in a more loyal following for religious groups. Case studies on Bapedi music and Gregorian chants are also reviewed and further support how religious music strengthens spiritual connections, fosters shared values, and contributes to cultural identity within religious communities. In conclusion, religious music profoundly influences an individual’s quality of life, emotions, and moral convictions and, therefore, has an enduring effect on shaping a religious experience for people.
Impact of Multiple Ethnic and Racial Identities on Mental Health and Environmental Influences
Authors: Tang Sienna , Kim Yubin
Published: August 2024

This study investigates the correlation between biracial and multiracial mental health, emotions, and identity development and the environments they mature. A growing biracial and multiethnic population faces unique challenges, and understanding their development is crucial for tailored support. A survey with 94 participants was conducted on factors such as cultural exposure, discrimination experiences, and family dynamics. Results reveal distinct challenges faced by multiracial individuals, impacting their mental health. Identity confusion is more prevalent among multiracial participants, indicating the complexity of navigating multiple cultural backgrounds. Household experiences vary, with unequal identification linked to disparities in parental cultural exposure. Challenges related to ethnic identity are higher for multiracial individuals, emphasizing the need for resilience and coping mechanisms. Mental health challenges correlate negatively, with Pearson coefficients of -0.45 and -0.23, with family support and cultural exposure, respectively, suggesting the importance of these factors in mitigating difficulties. The study contributes new insights, urging action to enhance mental health programs for individuals from diverse backgrounds.
ADHD and Film Watching: Unveiling the Impact on Attention, Confidence, and Cinematic Experience
Authors: Kim Ian , Genicoff Sharon
Published: August 2024

ADHD, a neurodevelopmental condition impacting millions globally, is characterized by symptoms such as inattention, hyperactivity, and impulsivity. While existing research has investigated ADHD in various contexts, this study sheds light on how individuals with ADHD perceive and engage with cinematic narratives, specifically examining overconfidence. Individuals overestimate their abilities and impulse activities on this cognitive bias, like skipping through videos. By examining the mental and emotional processes involved in comprehension among those with ADHD, this paper seeks to understand how overconfidence influences their interpretation of cinematic stories and characters. The literature review discusses the intricate connections between ADHD, attention deficits, impulsivity, and self-perception. ADHD patients tend to have deficits in attention span and self-perception, indicating a Positive Illusory Bias (PIB) where individuals overestimate their abilities. For the study, 300 participants were exposed to a video and assessed using the CAARS scale to measure ADHD symptomatology levels, perceived likeability of characters, confidence in judgments, and time spent watching the video. Results indicate a negative correlation between ADHD symptomatology and video watch time, with higher ADHD levels associated with earlier self-truncation. Mediation analysis suggests that participants' confidence in judgments mediates this relationship, with higher ADHD symptomatology linked to greater confidence and decreased video watch durations. The findings contribute to understanding how individuals with ADHD perceive and interact with cinematic storytelling, emphasizing the need for empathy and awareness in accommodating their unique perspectives and tendencies.
The Importance of Cybersecurity Measures in Business Operations to Combat Phishing Attacks
Authors: Bandapelli Anish , Prasad Dr. Sarada , Kethar Jothsna
Published: August 2024

In an era where digital threats loom larger with each passing day, the security of business supply chains has become paramount to ensuring operational integrity and maintaining competitive advantage. This research paper delves into the critical role of cybersecurity within the complex networks of modern supply chains, aiming to highlight the multifaceted challenges and the necessity of robust defense mechanisms. Through a comprehensive analysis, this study identifies the main vulnerabilities that jeopardize the security and privacy of data and assets across interconnected business chains. Employing a mixed-methods approach, this research paper combines quantitative data on recent cyber incidents with qualitative insights from cybersecurity experts to explore effective strategies for mitigating risks. The findings reveal a significant correlation between the implementation of advanced cybersecurity protocols and the resilience of supply chains against cyber threats. Moreover, this study underscores the importance of collaborative efforts and shared responsibility among all supply chain stakeholders in fostering a secure digital ecosystem. The implications of this research extend beyond theoretical contributions, offering practical guidelines for businesses to enhance their cybersecurity posture and safeguard their supply chains against evolving digital threats. This paper not only adds to the body of knowledge on supply chain security but also serves as a call to action for organizations to prioritize cybersecurity in their strategic planning processes, ensuring the protection and privacy of their operations in the digital age.
The Need for Cybersecurity in Automotive Industry
Authors: Prasanth Nair Anay , Gochhayat Sarada Prasad , Torremocha Virgel , Kethar Jothsna
Published: August 2024

This research paper analyzes the importance of cybersecurity in the automotive industry and explains how cybersecurity threats impact the modern world of connected, autonomous, and electrical vehicles. Furthermore, this paper covers in-depth research into the different types of automotive cyber threats such as physical, sensor, software, and network attacks on a vehicle, and various methods to prevent these threats from happening in real-world scenarios. The research paper also helps the reader to gain a broader understanding of cybersecurity in the automotive world. The methodology of this research involved analyzing and collecting information through various external websites, articles, and research papers. Information was collected on automotive cybersecurity incidents from news articles and international cyber security conference presentations. Based on this evidence from various external sources, the research problem was supported by factual data. This paper also talks about all the various solutions to cybersecurity threats in depth such as authentication and encryption, malware detection, software analysis, VPNs (Virtual Private Network), and cryptography. This paper also provides information about some of the new regulations and standards that are currently adopted in the automotive industry.  With the rapid change in automotive technology, it is important to be aware that cars can have up to 150 ECUs (Electronic Control Unit) and about 300 million lines of code in near future which could expose more vulnerabilities. (2020, McKinsey & Company). To meet the challenge of these automotive cyber security threats, it is important that awareness should be created among the product engineers and researchers working on future automobiles. This paper attempts at exploring the challenges with automotive cyber security and creating this awareness.
Exploring the Integration of Machine Learning and AI in the Treatment and Diagnosis of Communication Disorders
Authors: Oh Luke , Whitehead Gary
Published: August 2024

Communication disorders affect a significant portion of the population, and conventional diagnostic and treatment methods encounter challenges related to accessibility and efficiency. This paper examines how machine learning (ML) and artificial intelligence (AI) can be integrated into the diagnosis and treatment of communication disorders. The study emphasizes the potential transformation brought about by these technologies in improving patient outcomes, specifically highlighting successful initial diagnosis rates achieved with ML classifiers. Various novel ML methods developed over the years are discussed, offering the prospect of more precise treatment options for individuals with cognitive disabilities. However, the current research has limitations, including a narrow focus, the absence of a common gold standard, and difficulties in maintaining consistency across studies. Ethical considerations play a crucial role in the discussion, particularly concerning data privacy, security, and potential biases in ML models. The lack of diverse and representative datasets is noted as a factor that may lead to disparities in care. To advance the field, it is essential to replicate results across larger cohorts, address technical challenges, and improve the interpretability and transparency of AI-generated recommendations. These steps are identified as crucial for the ongoing development of ML and AI applications in the diagnosis and treatment of communication disorders.
How Murals Support Ukraine: Eastern Ukraine-Themed Murals Resembling Berlin Wall Murals
Authors: Shim Boram
Published: August 2024

In 2013, President Viktor Yanukovych suspended a trade pact between Ukraine and the EU, which led to the Euromaidan protests and violence from armed police. Since then, muralists have recorded the reality of Ukrainian politics and the growth of Ukrainians’ national identity. Through an exploration of art history, sociology, and the history of Ukraine, this research paper serves as an initial investigation of how newly created murals in Ukraine and past murals on the Berlin Wall convey the same theme of battle against communism and share similarity in the growth process. This paper’s comparative analysis was conducted in three stages: an examination of the art’s initial transformation from graffiti to mural form, an analysis of the works’ emphasis on patriotism, and an exploration of the portrayal of minorities, designed to attract an international audience. Findings indicate that murals depicting conflicts in Ukraine have the potential to reinforce Ukrainians, much as murals on the Berlin Wall were crucial in reunifying Germany during the Cold War.
Would Artificial Intelligence Methods Improve Early Diagnosis and Progress of Ovarian Cancer?
Authors: Yau Connor , Huang Yongmei
Published: August 2024

Ovarian cancer is one of the most common cancers in women, characterized by advanced-stage diagnosis, poor prognosis, and high mortality rate. The predominant screening methods rely on ultrasound images and carbohydrate antigen 125, which has limitations such as a lack of specificity. More insight is needed to understand the etiology of ovarian cancer. Machine learning offers a solution to some of these issues and can be applied in diagnosing and prognosing ovarian cancer. This review article collects information on how machine learning models can be trained on a variety of data types, such as biomarkers, clinical factors, and medical imaging, and how these models can be used to classify benign and malignant tumors, predict survival rates, and determine response to drugs and treatment. Overall, we found that machine learning methods have shown great potential in applications in ovarian cancer, but more research needs to be conducted to further advance machine learning technologies in clinical practices.
Evaluating Machine Learning Techniques for Web Robot Detection
Authors: Sirikonda Jayan , Zabihimayvan Mahdieh
Published: August 2024

A web robot, also known as a web crawler, is an automated script that systematically browses the World Wide Web in a methodical manner. Web robots are commonly used by search engines to index web pages, gather information, and update search engine databases. They follow hyperlinks from one web page to another, collecting data and information for various purposes such as indexing, data mining, and website monitoring. Web robot detection is the process of identifying and distinguishing between human users and web robots on websites, which are a major source of web traffic. This process is vital to prevent malicious web robots from having a negative effect on web servers’ traffic and their users’ privacy. Companies such as Imperva, Inc. use machine learning models to identify malicious bots and prevent them from having unauthorized access to an organization’s server. To understand the benefit that machine learning models bring to web robot detection, we used pre-published server log data to construct machine learning models on Orange (a Data Mining Software) and Python that can distinguish between malicious and benign web robots. We evaluated the performance of three well-known machine learning algorithms: kNN, neural network, and decision tree. Based on our experimental results, the neural network gains the highest precision and the lowest false-positive and false-negative percentage of web robots. However, the neural network requires more time to generate the desired output.
Understanding the Fundamental Reasons for the Growth of Pickleball
Authors: Gupta Kesh
Published: August 2024

It’s no secret that Pickleball as a sport has been rapidly increasing in popularity. Sometimes called “mini tennis”, this newer sport, incorporating the use of a wiffle ball and smaller plastic paddles, has reached record numbers in 2022 with participation growing 85.7% since 2021. Pickleball has surpassed sports such as soccer, tennis, and even basketball in the US. This paper seeks to analyze the reasons behind this rapid growth, as pickleball remains the fastest-growing sport in America for the fifth consecutive year. The results of this analysis conclude that the rapid rise in Pickleball is due to three main reasons: the sharp increase in pickleball participation due to the Covid Pandemic, where sports involving social distancing were more sought-out; the fact that people see Pickleball as less competitive and therefore more inclusive and enjoyable; and because the risk-benefit ratio works in favor for most recreational departments.
A MULTI-OMICS REVIEW OF GATA-3
Authors: Revanakara Yashas
Published: August 2024

Breast cancer is a prevalent mutation-caused disease that affects millions of people worldwide. One such gene that is related to causing the cancer is GATA3. This gene is a regulator of t-cell development and is also involved with causing luminal breast cancer when altered. The type of gene which GATA3 was never clear, displaying both properties of an oncogene and a tumor suppressor gene. The TCGA dataset provided by the Cancer Genome Atlas program provides relevant data through the numerous patients who have submitted data in the program. GATA3 has most of its mutations as truncation in both alleles with the X308_splice mutation. This recessive property suggests it is a tumor suppressor gene. Other mutations like the M293, a missense mutation, aren’t well researched but provide signs of being oncogenic. GATA# also initially increases survivability when highly altered proposing it to be a tumor suppressor gene. The role of GATA3 is still not well defined but shows most properties of a tumor suppressor gene.
Deep learning based anime character sketch quality recognition
Authors: Yanamadala Ishita , Dubey Atul , Rajagiri Divya
Published: August 2024

The explosion of the global anime market is poised to redefine the entertainment industry, boasting a valuation of USD 31.23 billion as of 2023 and projected to climb at a CAGR of 9.8% until 2030. At the heart of this growth is a vibrant community of artists and enthusiasts who navigate challenges such as artist scarcity and the lack of advanced tools for qualitative feedback and effective content promotion. Our study targets the core of anime creation sketching by evaluating the essential drawing elements and assessing their implementation in professional-quality anime portraits. We introduce an automated approach to predicting the anime quality using advances in deep learning. Utilizing transfer learning, we enhance three pre-trained models—MobileNetV2, ResNet50, and VGG16—with a customized dense layer, refining their capabilities for the binary classification of anime character sketches. The dataset employed comprises 155 images, categorized as 'Good' and 'Bad' to reflect the quality of sketches. A balanced split into training, validation, and testing subsets ensures a quantitative evaluation of data not seen previously by the model. The models, pre-trained on ImageNet and fine-tuned with our dataset, demonstrate varied sensitivity to hyperparameters, with the MobileNetV2 and ResNet50 model attaining a peak validation accuracy of 94% and the highest test accuracy of 79% indicating its potential as a robust tool for quality assessment in the anime industry. An interesting outcome of the research is that a lightweight MobileNetV2 model with much fewer parameters compared to other models resulted in the highest test accuracy.
Regulating Artificial Intelligence in the European Union and the United States
Authors: Wang Bryan , Haak Danielle
Published: August 2024

Ethics is an increasingly important topic surrounding the development and deployment of artificial intelligence (AI), which has had many impacts on the general population, as it is integrated into society. As a result, the European Union (EU) has already taken steps to regulate AI to prevent or limit the negative impacts imposed on the public while also attempting to promote their capacity for technological innovation; however, the United States (US) has not acted as swiftly and lacks major, tangible pieces of legislation for the regulation of AI. This research reviews the AI legislation development and implementation processes used by the EU and compares it to the processes used by the US when implementing similar laws and ideas in the development of its own regulatory framework. Critiques of the EU’s policies are assessed, and the political, economic, and social differences between the regulatory bodies are considered. This approach enables us to critically evaluate specific pieces of the EU’s legislation and recommend those that can be practically integrated into future US policies.
QUAS(AI)R: A Novel Machine Learning Algorithm to Predict X-ray Brightness in Active Galactic Nuclei
Authors: Kumar Deepthi , Rodriguez Tony
Published: August 2024

Active Galactic Nuclei (AGNs) are a compact region at the center of galaxies that emit more energy than the rest of the galaxy itself. They emit light across the electromagnetic spectrum, from radio waves to optical light to high-energy X-rays. AGNs indicate the existence of highly energetic phenomena in the nucleus of the galaxy. Although AGNs were identified 60 years ago, our knowledge about their physical properties is limited. Quasars, a subtype of AGNs, provide some of the most intense forms of X-rays, which are among the most energetic light known. Furthering our understanding of X-rays in the dynamic environments of quasars will add to our understanding of how to benefit from their use on Earth. In my project, I study the X-ray brightness in quasars to develop six types of regression-based machine learning models for the X-ray brightness predictions. These six models were Stochastic Gradient Descent (SGD), Random Forest, Ridge, Lasso, Bayesian and the baseline linear regression model, built on the scikit-learn Python package. The training/testing split on the MILLIQUAS dataset was 80/20 percent, and each model was tuned on model-specific hyperparameters. Benchmarked with the normalized mean absolute error (NMAE), the top three performing models were the Bayesian (0.022%), Ridge (0.180%), and Lasso (0.183%), with the baseline NMAE at 0.284%. With this, we can learn more about the evolution of galaxies in the early Universe and understand how these dynamic environments came to be.
Applying Artificial Intelligence in Diagnosis and Treatment of Autism Spectrum Disorder in Children
Authors: Nguyen Kelis , Huang Yongmei
Published: August 2024

Autism Spectrum Disorder (ASD) is a disorder of increasing prevalence that affects individuals socially, emotionally, and academically. The increased prevalence and restricted access to diagnosis and treatment suggest more efficient and widely accessible services are necessary. Many individuals with ASD do not receive proper attention due to various reasons, including costs, long wait lists, and long processes. Recent developments in artificial intelligence and machine learning are believed to be able to aid this accessibility issue. Research has shown progress in using MRI and EEG datasets to develop machine learning models in diagnosing ASD and potentially finding biomarkers using supervised and unsupervised ML techniques. AI algorithms analyzing body language and physical behaviors could potentially be used to assess ASD characteristics despite the heterogeneity of the disorder. The adaptivity of artificial intelligence is believed to have the potential to create supportive software for students with ASD to support learning, emotional regulation, and development of social communication skills and increased adaptability. More evidence is required to prove the effectiveness of these applications, but many studies show a lot of promise for children with ASD.
Evaluating the Impacts of Parasomnias on Sleep and Exploring Their Therapeutic Interventions
Authors: Chinala Vishwaj , Su Chang
Published: August 2024

Sleep disorders are topics of important discussion as they apply to a function crucial for the growth and development of individuals. Sleep plays a pivotal role in one's long-term health but various disorders may limit or prevent high-quality sleep for children and adults. One of these common disorder groups is parasomnias. As there is still much to learn in medicine when it comes to parasomnias and their treatments, it is of utmost importance to understand the processes behind the occurrence of parasomnias and their associated conditions. This review breaks down the most common parasomnias, categorizing them based on the onset of disease during rapid eye movement (REM) or non-rapid eye movement (NREM) sleep stages. This includes somnambulism (sleepwalking), confusional arousals, night terrors, REM behavior disorder (RBD), nightmare disorder, and others. It highlights the characteristics of diagnosis for these individual disorders and attempts to evaluate them through an etiological, epidemiological, and pathophysiological lens. This review also presents the current pharmacological and non-pharmacological interventions for these disorders.
Examining the Effects of Fracking on Groundwater Using the United States Fracking Well Data
Authors: Lin William , Koch Magaly
Published: August 2024

Ever since fracking technology, drilling downward and then horizontally, has been developed to extract oil and gas from underground bedrock, the number of fracking wells has been drilled at an accelerating speed, and the amount of underground water consumed has also increased enormously. The goal of this paper is to address this alarming environmental and climate threat from the proliferation of fracking wells accompanied by a surge in groundwater consumption, through analyzing the up-to-date registry data available as of October 2023 from FracFocus.org on the fracking wells nationwide . The paper starts with a literature review to study the existing research findings on fracking water consumption issues. Next it provides a comprehensive data analysis of the water consumption by fracking wells at both national and state levels. It also explores the relationships between the water consumption with various other factors such as the vertical depth of the wells, the chemical ingredients of the fracking fluid, and the purposes of the additives, and aims to provide insights from the correlation and causality analyses that may offer potential strategies to reduce groundwater consumption in the fracking industry. In addition, the paper employs machine learning techniques such as Random Forest model to explore using predictive models to identify wells that have high likelihood of causing a water contamination issue so that proactive controls can be developed to reduce the occurrence of water quality issues.
A Novel Model to Minimize Time Series Forecasting Error using Convolutional Attention
Authors: Pathak Pranav , Plymire Daniel
Published: August 2024

The main consideration of this paper is time series forecasting using machine learning methods. While hundreds of studies have been conducted on this topic, we propose a novel, yet intuitive model, using technical indicators in combination with a focus on lagged relationships and the machine learning technique called BEDCA (Belief Encoder-Decoder with Convolutional Attention). BEDCA takes into consideration technical indicators to improve predictive accuracy. To evaluate our model, we used historical Coca-Cola stock data, room temperature data, and daily data including the number of COVID-19 case data. We found that our model trained relatively quickly and had a high accuracy (low loss). Overall, BEDCA outperformed the standard Long-Short Term Memory (LSTM) network on all three datasets, achieving MAE reduction of 74.1%, 41.3%, and 69.0%, respectively, although it took 3.05, 3.26, and 3.20 times longer to train on each dataset respectively. Our results highlight that convolutional attention is a promising form of attention and that technical indicators are important considerations for time series data.