bluuebunny/crossref_metadata_2025 huggingface dataset:
A Narrative Review of the Influence of Personality on Athletic Success
Authors: Sarracino Isabella
Published: August 2024

Since the creation of the Olympic Games over two millennia ago, athletics has been a way to discover humanity’s physical limits. Through training and practice, athletes can achieve feats never reached before. However, as competition becomes more difficult, athletes and coaches have to find new ways to break through barriers and reach new heights in their sport. At high levels of competition, mental training becomes just as important as physical training. By understanding an athlete’s personality characteristics and how it can affect performance, competitors and coaches alike can develop and utilize these traits to further athletic success. Through an analysis of various studies, four characteristics have been studied in depth: extroversion, conscientiousness, intrinsic and extrinsic motivation, and self-efficacy. All of these attributes are generally beneficial for an athlete to possess, but each has its drawbacks and nuances. This paper outlines how each facet of personality can affect an athlete and how athletes and coaches can develop these characteristics for success.
Neural Radiance Fields (NeRF) for 3D Visualization Rendering Based on 2D Images
Authors: Cyriac Joann
Published: August 2024

We are living in a 3D world, but oftentimes we visualize things in 2D scopes like in pictures and videos of different objects. The world of 3D visualization has been constantly evolving over the past few years, still growing with the introduction of cutting-edge computer vision and deep learning methods. Reconstructing 3D scenes and being able to interact with them in virtual environments used to be something beyond our capabilities but now with the use of Neural Radiance Fields (NeRF) and Instant Neural Graphic Primitives (InstantNGP) we are able to create a world in a virtual space, opening the gateways to applications in object detection and generation, augmented reality, medical imaging, and more. Access to these methods will allow for more immersive development by creators, hands-on educational resources, and more experimentation and research, stimulating future applications and techniques.
Cross-Domain Transfer Learning for Medical Condition Classification from Infant X-ray Images
Authors: Park Seyoung , Martin Joe
Published: August 2024

Field of artificial intelligence technology has flourished in recent years which led to a creation of diagnosing or assessing diseases using X-ray images. On the downside, these creations focus mostly on adult X-ray images and can not accurately diagnose infant X-ray images. This issue stems from a combination of factors: limited availability of datasets containing infant X-ray and significant variation in these images due to the rapid development of the infant body. Therefore, there is a high demand to develop comprehensive solutions that address these challenges and provide accurate insights. The proposed representation learning-based framework comprises two stages: auto-encoder-based representation learning and transfer learning for diagnosis. The first stage uses adult X-ray images to train the model for improved representation, generating identical reconstructed images. The second stage utilizes pre-trained models to diagnose diseases and predict infant age, enhancing accuracy by accounting for age-related variations in X-ray shapes. This innovative approach represents the first endeavor in unrestricted pediatric X-ray diagnosis, utilizing self-supervised learning for enhanced accuracy. As a result, the comprehensive and extensive experiment allows the proposed method to outperform in comparison to the existing methods. I expect that my research will contribute to the pediatric field of medicine and serve as the foundation of diverging the utility of artificial intelligence.
Spatio-functional annotation of ADHD risk loci in human brain
Authors: Nashid Jeremy , Clarence Tereza
Published: August 2024

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity. Recent genomic advances have identified genetic loci associated with an increased risk of ADHD. While these loci offer insights into ADHD's genetic underpinnings, understanding their implications in the human brain remains a challenge. This study presents a comprehensive spatio-functional annotation of the recently proposed 27 ADHD risk loci within the human brain.
By utilizing gene enrichment analysis, a method to identify and analyze gene functions and interactions, we can deepen our understanding of the molecular pathways and functional roles of genes linked to ADHD. This analysis uncovers diverse functions among genes associated with ADHD, spanning neuronal activities, transcriptional regulation, receptor signaling, immune responses, and more. Additionally, we explore the spatial context of these genes through the available Hi-C datasets. By examining their spatial localization and potential regulatory interactions, we gain insights into how these risk loci might contribute to ADHD etiology at the chromatin and gene regulation levels.
These findings not only enhance our comprehension of ADHD's genetic basis but may also hold promise for potential future targeted therapeutic strategies. By unraveling the intricate interplay between genetics, brain function, and ADHD, this research opens avenues for precise interventions that could benefit individuals affected by this disorder. Ultimately, this study bridges the gap between genetic discoveries and spatial organization or genome, offering insights into the complexity of ADHD and potential directions for future research.
In Silico Screening and Evolution of Promising Novel Anti-Virulents Against Salmonella ATPase
Authors: Xu Lexi , Shen Norah
Published: August 2024

Current treatments for bacterial infections are under threat by the growth of antibiotic resistance in many different pathogens. Of these pathogens, Salmonella is a particularly widespread microbe, infecting over a million people annually as the leading source of food-borne diseases. One potential solution for antibiotic-resistant Salmonella is virulence inhibition of the bacteria’s T3SS injection system, which has been shown to destroy Salmonella’s proliferative abilities. Here, we identify fourteen compounds, primarily novel ligands, that exhibit high in-vitro potential as Salmonella inhibitors by attacking the ATPase InvC protein vital for T3SS injection–an enzyme that has not been previously evaluated for small-molecule inhibition. We also present a statistical analysis of AutoGrow4, a virtual structure-based molecular design tool that evolves ligands to better suit a target protein using Autodock Vina binding affinity calculations. Together, these create an entirely open-source workflow towards computational identification and evaluation of novel chemical treatments.
The effects of pressure and release training on equine behavior and discipline
Authors: Edwards Addison , Vajner Deborah
Published: August 2024

In America, there are over 100,000 horse-related injuries per year (Mendell, 2006). It is important that all horses are properly trained to avoid dangerous accidents resulting from poor training involving the horse, rider, or spectators. This study aims to understand how a person should approach properly training a horse. It determines what training methods are the most effective and why it is important that owners understand how horses process discipline. Previous research indicates the common need for pressure, but fails to explore it as a training method. As such, a definite answer that explains how Pressure and Release Training affects equine behavior and discipline has not been determined. A physical experiment collecting and analyzing quantitative data was conducted in order to obtain the most accurate and specific results. Horses of various breeds, ages, sexes, and training levels were subjected to five different Pressure and Release Training exercises and scored on their completion (attempt > accuracy). Each horse was tested twice on separate days to ensure replicability. All horses completed every exercise successfully without conflict behaviors or refusal (50/50). The experiment reveals that regardless of traits, Pressure and Release Training is an effective training method that horses understand even without previously consistent training. However, it is vital that trainers understand how horses process discipline so that they know if the training is effective and low stress for the horse.
Children’s IQ Reduction Due to Prenatal Fish MeHg Exposure in Coastal Areas: A Comparative Analysis
Authors: Kuang Ziheng , Chen Shujuan
Published: August 2024

Maternal dietary exposure to heavy metals like methylmercury (MeHg) through fish consumption has long been associated with neurological disabilities, including Intelligence Quotient (IQ) reduction. The most common exposure pathway to MeHg is dietary intake through fish consumption. However, there has been limited research comparing the IQ reduction caused by different fish species in different coastal areas. As Kuwait, Hong Kong, and Shanghai are three coastal areas with different cuisines and fish consumption rates, they are chosen as sites where the health risk of children’s IQ reduction due to the consumption of fish is to be assessed in this analysis. The results of this study suggested that among the fifteen types of seafood examined in Kuwait, hamoor was determined to be the most dangerous species, causing the highest mean IQ reductions in Kuwait. Frozen cod fillet was estimated to cause the highest mean and median IQ reductions amongst the twenty-six seafood types in Hong Kong, China. River eel and sardine are considered to be capable of inflicting the highest mean and maximum IQ reductions in Shanghai. Overall, seafood in Kuwait is more prone to causing serious intellectual disabilities because of its highest mean IQ reduction on average amongst the three locations examined. On the other hand, most types of seafood in Hong Kong and Shanghai have much lower mean IQ reductions, and are thus considered generally safe for consumption.
Exploring the Femme Fatale: How does the Little Black Dress paint the modern female persona?
Authors: Ren Shirley
Published: August 2024

Coco Chanel's initially designed the iconic Little Black Dress in the 1920s, and the fashion industry has since reproduced and reinvented its characteristics. This paper aims to explore the nuances of how modern ideas of feminism interact with the femme fatale identity associated with the LBD through both affirmation and disagreement. I begin by establishing the traditional French roots of the femme fatale persona using literary work and appropriate cultural context. The LBD's evolution is then presented with an exhibition in the MoMa, and paired with Audrey Hepburn's American reinterpretation and Princess Diana's revenge dress. The classic painting the Birth of Venus and Plato's philosophies are used to explain the possible hypersexual direction of the evolution. The modern LBD is shown through a dress from the fashion chain Aritzia, which demonstrates current issues such as consumerism and objectification of the female body. This paper ultimately reveals how fashion can both mirror widespread cultural beliefs during its time, and provoke social change with its implications.
Analyzing Single Nucleotide Polymorphisms and Their Chromosomal Distribution to Unravel the Genetic Landscape of Melanoma
Authors: Huang Angelina , Aguilar Roberto
Published: August 2024

Melanoma, a malignant skin cancer, continues to pose significant challenges in early detection, optimal treatment strategies, and understanding its etiology. This study aimed to elucidate the complex genetic landscape of melanoma by analyzing 2,641 Single Nucleotide Polymorphisms (SNPs) across 18 genomic variant categories within a melanoma cohort. The analysis revealed diverse genetic variations, including Missense and Frameshift Variants, with a significant number of unknown variants that require further exploration. A significant association (p < 0.05) was found between the identified variants and the melanoma cohort. The study's findings underscore the heterogeneous nature of melanoma and emphasize the potential need for personalized therapeutic approaches. This comprehensive study contributes valuable insights into melanoma's genetic landscape and holds promise for future interventions and personalized prevention and treatment strategies.
A Deep Learning Framework for Diagnosis and Survival Prognosis of Central Nervous System Tumors
Authors: Sreedhar Diya , David Ashok
Published: August 2024

Diagnosis and prognosis of central nervous system (CNS) tumors rely on manual and algorithmic approaches that are subject to variations, inefficiencies and bias. With 5-year survival rates as low as 6%, timely tumor assessment is crucial to ensure patient health. Machine learning and deep learning techniques have been used over the past decade to reduce the assessment burden and augment physician diagnoses. Traditional machine learning models require manual feature extraction and engineering, introducing further variability of the results. On the other hand, automated deep learning models require complicated engineering and workflow implementations that need to be managed and updated for new datasets. This study endeavors to define a generalizable, two-phase neural network pipeline and user interface that can be applied to clinical assessment of any tumor with 3D MRI scans, allowing for improved personalization of patient diagnosis and treatment options. In the first diagnostic phase, radiomic features are used to predict tumor severity and extent. The outputs of this phase are then passed to a survival prognosis model along with the patients’ clinical data to predict the 5-year overall survival rate. The diagnostic model achieved a F1-Score, a measure of classification accuracy, of 94% and the prognostic model achieved a risk-adjusted, time-dependent Harrell’s Concordance Index score of 0.92, indicating the framework’s generalizability to new datasets. The mobile app developed as part of this study offer ease of access to physicians and radiologists in reviewing predicted results and subsequent patient interactions.
Are zinc fingers of α-proteobacteria an important molecular mechanism?
Authors: Jakubowicz Pola , Tribe Nicola
Published: August 2024

The zinc-finger proteins in α-proteobacteria are considered to be the precursors of zinc fingers in higher Eukaryotes, due to their structural similarities and the crucial role played by α-proteobacteria during eukaryogenesis. Since they interact with macromolecules, their emergence in Bacteria, and later in the Eukarya, provided those organisms with major functional upgrades and evolutionary advantages. Hence, the aim of this research is to prove the important role of α-proteobacterial zinc fingers in this taxon, and the significance of their relics, present in higher Eukaryotes. A part of this investigation involved identifying similar zinc-finger proteins between α-proteobacteria and humans using the blastp algorithm, finding their shared domains in the CDD database, and modelling their structures in the ChimeraX programme. The remaining research objectives were reached by analysing data from pre-existing studies, in order to come up with an additional set of conclusions, relevant to this investigation. It was established that the Ros/MucR zinc-finger proteins regulate many pathways, crucial for the survival of α-proteobacteria and their interactions with Eukaryotes. Additionally, it was found that many zinc-finger proteins supply α-proteobacteria with eukaryotic mechanisms, which differentiate them from other bacterial taxa. It was also concluded that α-proteobacterial zinc fingers may be responsible for the resistance of α-proteobacteria to certain heavy metals. This investigation also proposes a new evolutionary hypothesis for the emergence of zinc fingers in Proteobacteria, and presents further arguments in favour of the theory that the Eukaryota acquired zinc fingers from α-proteobacteria during eukaryogenesis.
Lithium: The Most Critical Mineral
Authors: Sharma Avyakta
Published: August 2024

Lithium is a very critical mineral in today’s economy. With a newfound interest in lithium for the growing market of batteries for electric vehicles, accessible lithium with traditional mining methods is not enough to keep up with this emerging demand. Traditional lithium mining techniques include hard-rock mining and solar evaporation from brine ponds, but new ways include extracting lithium from brine directly without additional process steps – termed Direct Lithium Extraction (DLE). This paper provides a history and overview of lithium materials as well as insight into the emerging techniques involved in DLE and how they affect the lithium supply chain and lithium battery market. Additionally, the various market players and how they approach the emergence of DLE technologies will be covered.
Exploration of Mental Disorders in Greek Mythology: An Analysis of Ancient Archetypes
Authors: Cho Claire , Salerno Christine
Published: August 2024

Mental disorders remain a stigmatized and seldom-discussed subject in contemporary society, where widespread misunderstanding and lack of awareness persist. The purpose of this research is to form archetypes of mental illnesses and symptoms to allow society to learn about and emphasize more easily with those who are diagnosed with them. It analyzes the behaviors and experiences of characters from Greek mythology and connects them to specific mental disorders in the present day. Texts such as the Odyssey and Metamorphoses were used to understand the actions of characters such as Narcissus, Odysseus, Orpheus, Medea, and others. These behaviors were compared to the psychological symptoms of disorders such as Narcissistic Personality Disorder, Post-Traumatic Stress Disorder, Depression, and others. While the ambiguity of Greek mythology and lack of professional diagnosis cannot fully confirm these characters’ disorders, it can still create a unique perspective for individuals to understand mental illnesses. As a result, mental disorders can be recognized and explored by those who are less informed about the topic.
The Mind and the Money: Determining How the Mental Health of a Nation Influences its Economic Prosperity
Authors: Serrano Adrián , Lopez Johnny
Published: August 2024

The purpose of this investigation is to analyze the relationship between the general mental health of a population and how it can affect the economy of that population's nation. With the severity of mental health being more and more acknowledged in today's world, it is crucial to understand how mental diseases can affect economies in order to properly guide laws and healthcare on the mental health. Through a rigorous electronic search, a total of 10 articles were analyzed closely to investigate this relationship. After weeks of analysis, this study found that there is a considerable influence that the mental health of people can have on their economy, regardless of its size. It is stressed that decision-makers acknowledge the importance of mental health in affecting economies, and act accordingly to provide a better outcome for everyone.
The Effect of Noise Pollution on Pollinator Biodiversity
Authors: Franks Liliana , Farquhar Samantha
Published: August 2024

Pollinators are a crucial aspect of maintaining our environment and provide virtually all of the food humans consume in order to survive on this planet. However, as the earth becomes increasingly populous, the threats that pollinators and other organisms face today are becoming more vulnerable. Noise pollution, despite having extensive consequences on local wildlife, has remained a subject relatively unstudied. My study aims to provide a greater understanding of the threats pollinators face today by analyzing the relationship between noise and pollinator biodiversity. To do this, in my study I compare a wilderness trail and a large music venue. My research determines whether the presence of noise affects pollinator biodiversity in the surrounding area and measures the significance of these effects. By using scientific observation, data collection, and scientific analysis, my study investigates and evaluates whether or not there is a correlation between exposure to high intensity noise and pollinator biodiversity.
Student and Societal Pressures: Exploring Causes, Consequences and Potential Stress-Reduction Strategies
Authors: Ruiz Jorge , Lopez Johnny
Published: August 2024

This investigation aims to explore the causes and consequences of student stress in order to design practical strategies that parents, teachers, and schools can use to mitigate stress. In today’s society, many students face high societal pressures and expectations. The desire to meet these high expectations can often be extremely stressful, affecting a student’s mental health and well-being. Understanding and addressing this issue is essential to effectively create a supportive, stress-free environment where students can balance academic achievement and well-being. Some of the consequences of stress include poor academic performance, insufficient sleep, anxiety, depression, physical illness, irritability, and social withdrawal. The investigation results also identified five components that make up a stress-free environment. The components are positive parental involvement, a learning-based, non-competitive school environment, positive peer relationships, and teacher motivation and behavior. Additionally, the results suggest that implementing Social-Emotional Learning courses in schools is an excellent strategy to help students manage stress and foster positive peer relationships.
Enhancing China’s Soft Powers with Traditional Chinese Medicine and Global Healthcare Integration
Authors: Seo Christine , Fillebrown Catherine
Published: August 2024

During a time where global influence is increasingly shaped by cultural engagement and soft power, Traditional Chinese Medicine (TCM) is a pivotal element in elevating China’s global standing and soft powers. Beyond its role as an alternate medical practice, TCM’s deep roots in Chinese culture and philosophy serve as its strengths to enhance China’s global stature. This study looks at TCM’s potential as a catalyst for bolstering China’s soft powers by examining the historical context, contribution to cultural diplomacy, challenges in global integration and strategic importance with the Belt and Road Initiative (BRI). It discusses the significant market share of TCM and its potential as a key economic driver, alongside the scientific scrutiny it faces under Western medical standards. The role of TCM in Chinese healthcare and its increasing scientific credibility in the Western world are analyzed, emphasizing the need for a nuanced approach to integrate TCM in global healthcare. In conclusion, the study evaluates the potential of TCM as a tool for cultural exchange, understanding, and influence, proposing strategies for its international promotion and integration into global healthcare practices. It emphasizes the opportunity for China to bolster its soft power and demonstrate its commitment to global healthcare innovation through TCM. The paper suggests that TCM, with its unique health and wellness approach, can contribute significantly to global health improvements and position China as a key player in the international healthcare domain.
How does power and globalization impact the equality and ease of immigration?
Authors: Nunez Maneli , Buehler Chris
Published: August 2024

This paper presents research on immigration and the international theory surrounding it. The legal versus illegal immigration and the different schools of thought are delved into and explored. An investigation with a local organization was conducted to measure the immediate impacts of immigration laws on the local community and how economic factors impact the success of immigrants on the path to citizenship.
A Proposal to Revive the Brewster Wheeler Recreation Center: An Architectural Approach
Authors: Lee Ian , Aragüez José
Published: August 2024

This research dives into the historical roots and proposed revitalization of the Brewster Wheeler Recreation Center, located in downtown Detroit with a focus on the city's progress and the preservation of its heritage. Almost all previous renovation plans for the city of Detroit have come to a halt due to the lasting effects of COVID-19. However, as the pandemic has come to an end, Detroit has been making huge leaps in its revitalization efforts. The paper’s main objective is to contribute to the city of Detroit and have applications to the real world. The proposal presents a pitched roof design that shows remnants of the building’s historicalness and highlights its novelty, creating a balanced blend between the two. The proposal also takes advantage of the expansive surrounding land to position the recreation center as a central community hub by including sports fields and a historical museum inside the building. The museum would serve as the history of the building’s evolution and notable figures from its humble beginnings as a library in 1917 to its present state.
Disease Prediction Using Machine Learning Methods
Authors: Zhuang Christina , Ramezani Ramin
Published: August 2024

A visit to the doctor’s office usually starts with the nurse collecting patient symptoms, health information, and necessary lab tests. All the information will be presented to the doctor, and the doctor may collect additional information in order to do the right diagnosis. The doctor’s brain is like a complicated machine capable of quick processing of the information, relating it to previous patients, and mapping the information to diagnoses. This process resembles much to how machine learning works. In this article, we explore how machine learning could help predict different diseases and facilitate a doctor’s diagnosis. In particular, our study focuses on unbalanced, multiclass classification problems.
Burnout: A Silent Rising Phenomenon
Authors: Prabhakaran Aishwarya , Burton Shannon
Published: August 2024

Burnout is an increasing issue in our post-pandemic world. Since the 1970’s we have recognized the causes, theconsequences, and even potential solutions of burnout. New research dives in even further by categorizing the threecause of burnout, the several measurement tools that have developed since the 1970's, and the expandingdemographic that is affected by burnout.
Gender Stereotypes and Health of Individuals in China
Authors: Xin Huijing
Published: August 2024

Existing literature highlights the impact of traditional gender dynamics and stereotypes on individuals worldwide, yet their connection to health, particularly for individuals with different education levels and genders within China, remains unexplored. This study aims to examine the mental and physical health effects of gender stereotypes on diverse genders and social classes in China. It builds on previous research's insights and addresses questions about the extent of these impacts and their variations across education levels and genders. The study utilizes the 2017 Chinese General Social Survey data to explore the relationship between acceptance of gender stereotypes and health status, specifically among married individuals. The findings reveal a negative correlation between gender stereotypes and individual well-being, particularly impacting women and varying across education levels and genders. Educated women in China show increased resistance to stereotypes and challenging traditional roles, while men's attitudes are less influenced by education. These insights emphasize the importance of education in disrupting traditional gender norms and promoting well-being. Further research is recommended for cross-country comparisons and greater involvement of unmarried individuals' perspectives.
A Mixed-Integer Linear Programming Approach to Optimize Tennis Regimens for Young Athletes in Korea
Authors: Jeong Younha , Legat Benoît
Published: August 2024

The popularity of tennis has been surging among young individuals in South Korea, as it offers an appealing alternative to other sports like golf. With this surge in popularity, there is a corresponding increase in competitive intensity, making the optimization of training regimens crucial for success. While athletes in this sport commonly engage in a variety of training activities there has been limited systematic research that offers a quantitatively validated approach for balancing these activities to maximize on-court performance. This study aims to fill this gap by providing an optimization model tailored to the specific requirements of tennis training. Through surveying 48 prospective athletes in South Korea, the study establishes upper and lower bounds for each of 4 activities: Serving Practice, Ground Strokes Practice, Physical Conditioning, and Mental Training. Multiple regression analysis is then used to calculate the weights of each training activity in an objective function, formulated to measure on-court performance. A Mixed-Integer Linear Programming (MILP) model is employed to allocate optimal weekly training hours to each activity, based on these weights and constraints. The study concludes by identifying an optimal training schedule that maximizes the value of the objective function, thereby optimizing on-court performance. The convergence of the objective and best bound at a low level further substantiates the optimality of the discovered solution.
Computer Vision Based AI for Control of High Velocity Autonomous Vehicle with Lane Detection
Authors: Aaron Jeyasingh Jeffrey , Smoragiewicz Tony
Published: August 2024

Line following algorithms often fail to account for the position of the vehicle within the lane, only depending on the curvature of the lane to determine steering output. For example, with previous algorithms, the vehicle could be to the right of the lane and the steering angle from the algorithm’s output would not correctly return the car to the center of the lane. Additionally, error can result from incorrect calibration: if the vehicle is truly pointing at a steering angle of 61 degrees when the correct angle should be 60 degrees, the vehicle will drift 1 degree throughout the turn. The error accumulates and ultimately will cause the vehicle to leave the lane. By fitting a polynomial curve to the input image data, the exact location of the line can be determined relative to the vehicle. Furthermore, the center of the lane can be pinpointed, offset calculated, and trajectory modified to smoothly return to the center of the lane.
GENOME EDITING TECHNOLOGIES IN THE TREATMENT OF DIABETES
Authors: Patel Dhrishit
Published: August 2024

This research paper will first introduce diabetes as a disease and briefly talk about all three types of diabetes as well. The symptoms, diagnosis, and consequences of having diabetes will also be discussed. Later, how the research question, “What are the various types of genome editing technologies that can be used to treat diabetes and what are their consequences?”, relates to diabetes and why it is important would be explained, along with a possible hypothesis that it could answer. In addition to that, how the research question tries to resolve the problem of diabetes would be underlined and a few solutions would be skimmed upon, which are the genome editing technologies: ZFN, TALENS, and CRISPR.
A review of the mechanics of dragline spider silk
Authors: Sandstrom Leo
Published: August 2024

Spider silks demonstrate extraordinary mechanical performance. They rely on an intricate hierarchical structure that gives rise to unique properties. Of the many types of spider silks that are produced, dragline spider silk has attracted the most research attention due its extremely high strength. Since data on dragline spider silk is readily available, much can be understood about the nature of spider silk by analyzing the structure of dragline spider silk. Moreover, the study of spider silk can inspire the design of new materials. Here, we review the structure of dragline silk, present a particular material model to explain their behavior, and discuss the potential outlook in the area.
Analysis of the Uyghur Cultural Genocide
Authors: Kothari Nikhil
Published: August 2024

Despite the news coverage about the Uyghurs, there is limited information about historical events that shaped the Uyghur cultural genocide and how the crisis fits into the broader scope of ethnic minorities in China. This paper examines the role of Han Chinese ethnocentrism in the Chinese government's treatment of the Uyghurs. The Uyghurs have experienced an eradication of their way of life and evaluation of evidence from personal correspondence, diaries, speeches, and other primary and secondary sources, assists in deepening understanding of the genocide. Although the oppression is rooted in key events in the twentieth century, it was challenging to find primary sources due to CCP censorship and many documents written in Mandarin. Nevertheless, extensive research led to useful sources for analysis. The Uyghur cultural genocide is an important topic to study as it is a human rights crisis that continues to unfold today.
Exploring the Interconnected Mechanisms of Transgenerational Epigenetic Inheritance
Authors: Muthuswamy Sampriti , Katchur Nicole
Published: August 2024

It is estimated that around 70% of all adults around the world have faced trauma in their lives (Benjet et al., 2016). Trauma can cause individuals to undergo epigenetic changes which can lead to health complications in the future (Alegría-Torres et al., 2011). Epigenetics is defined as the study of molecular modifications to DNA through DNA methylation, histone modifications, and non-coding RNAs that can regulate gene expression independent of DNA sequences (Li, 2021). New evidence suggests that epigenetic changes may be passed down to offspring. However, the exact pathway for transgenerational epigenetic inheritance to occur is unknown. While existing theories, including intrauterine programming, miRNA-mediated pathways, and genomic imprinting, offer possible pathways, none can fully account for the spectrum of transgenerational inheritance. This paper will review the three models proposed and will explore the possibility of their combined influence on transgenerational epigenetic inheritance. Exploring epigenetic mechanisms can help offer potential intervention points to relieve the negative impact of trauma on several generations.
Applications and Advancements in Stem Cells for Cardiac Tissue Engineering
Authors: Jin Bora
Published: August 2024

Heart disease has continued to be the leading cause of death in the world (Mc Namara et al., 2019). Despite the growing need for a therapeutic, regenerative medicine approach, there has been a lack of advancements resulting in possible treatment options for these patients. Researchers have developed various approaches to combat the adversities of heart disease, ranging from small-scale cardiac patches to large-scale whole organ regeneration approaches. Although these findings are still preliminary, they provide potential therapeutic approaches. The main ingredient towards this promising direction are stem cell-derived cardiomyocytes, in which induced pluripotent stem cells are reprogrammed through signaling pathways to mimic characteristics of mature cardiac cells. With these self-renewing cardiac cells, researchers have been able to formulate hydrogel patches that mimic the environment of the cardiac tissue to eventually mend the injury and pump synchronously. For deeper penetration and favored nutrient flow, a process known as FRESH 2.0 has created a ventricular scaffold printing method that aids in the oxygen, nutrients, and signaling flow for bioprinted organ parts (Lee et al., 2019). In developing these tissues, SWIFT has progressed the needed cellular density to produce a cardiomyocyte rich artificial organ (Skylar-Scott et al., 2019). These successes still face drawbacks with perfusable vascularization, cell density on a whole organ scale, consistent maturation of cardiomyocytes, and clinical applications. Nonetheless, researchers have made significant advancements for regenerative medicine regarding the cardiac tissue, and will continue to expand their look towards a successful replication of the whole organ.
Cryogenic Liquid Rocket and Nuclear Thermal Rocket: A Survey of the Current Technology and a Comparison for Future Mars Missions
Authors: Wang Junchao , Baumard Vincent
Published: August 2024

The innovation of reusable rockets holds the potential to revolutionize both Mars scientific exploration and commercial tourism. Thus, this paper aims to indicate the most promising propulsion system for the Mars mission. While SpaceX proposed the cryogenic liquid rocket Starship to achieve Mars colonization by 2050, the viable nuclear thermal propulsion has been outlined by rocketry experts for space travel. This paper surveys the current state-of-the-art of both popular propulsion systems, indicating their advantages and disadvantages. Methods to increase the delivery and cost efficiency are also offered, including on-orbit refueling for cryogenic liquid rockets and various nuclear core variants for nuclear thermal rockets. Subsequently, their comparative analysis is delivered in the discussion, focusing on their transportation efficiency and cost efficiency for future mass missions. With faster travel, greater payload, and reduced propellant consumption, nuclear thermal propulsion has shown to be a brighter prospect and potential than cryogenic liquid rocket. Nevertheless, the author believes a new class of bimodal nuclear thermal electric propulsion system is the most promising option, combining the merits and complementing drawbacks of cryogenic liquid propulsion, nuclear thermal propulsion, and nuclear electric propulsion.
Aircraft-Produced Sonic Booms and Marine Life
Authors: Ghosh Siddharth , Bressman Noah
Published: August 2024

With the advent of new-age supersonic aircraft such as NASA’s X-59, Boom Supersonic’s Overture, and Spike Aerospace’s S-512, there is an emergent need to investigate their environmental consequences. This review evaluates existing research and information to determine what is currently known about supersonic aircraft and their potential impacts on marine life through a three-step process. 1) Determining possible characteristics of sonic booms produced by the aircraft: the aircraft being evaluated in this article produce low-frequency (0.1-100 Hz) sounds with surface PLdBs (perceived surface loudness decibel levels) around 70-75 dB. 2) Assessing the behavior of similar sounds as they interact with the ocean: sonic booms have been observed to penetrate the ocean for 98-164 ft, but this depth can vary due to differences between the experimental and real-life flight conditions. A separate effect, produced by sonic booms’ contact with the ocean surface, may penetrate deeper as infrasound. 3) Evaluating the potential consequences of these sounds on marine life: the continuous production of sonic booms along flight paths worldwide raises concerns about possible noise pollution, adding to the existing issue of oceanic anthropogenic noise pollution. Existing literature on underwater noise pollution—with similar frequencies and comparable intensities—shows negative impacts on marine reproduction, communication, stress levels, and physical health. This review contributes to broader ongoing discussions surrounding the resurgence of supersonic aviation and its environmental impact. Further research is necessary to assess the impact that transoceanic supersonic flight could have on marine ecosystems to truly determine supersonic flight’s environmental footprint.
The Epigenetics of Cocaine Addiction: An Analysis of D1 and D2 Dopamine Receptor-Expressing Neurons
Authors: Lee Daniel
Published: August 2024

Cocaine addiction is one of the most common substance use disorders worldwide. Currently, there are no medications approved by the U.S. Food and Drug Administration to treat cocaine addiction. Repeated exposure to cocaine induces long-term changes in the brain, resulting in increased displeasure and negative moods when not taking the drug. Cocaine abuse causes persistent plasticity, altering the composition and levels of membrane receptors involved in neuronal signaling pathways. This leads to long-term, stable changes in synaptic connectivity and strength in the dopaminergic system, particularly involving the D1 and D2 Dopamine Receptor-Expressing Neurons [DRD1 and DRD2]. These two cell types comprise 95% of the NAc, a key node of the brain’s reward circuitry whose role is to translate motivation into action. Previously, there have been limited systematic or quantitative studies on the differences in structure between DRD1 and DRD2 neurons in the context of drug addiction. This paper mapped out the 3D structure of the DRD1 and DRD2 genome, identified the regions most affected by the epigenetic mechanisms of cocaine treatment, and quantified the subsequent changes in gene expression. Future experiments and studies can expand upon the results from this research to target specific regions of chromatin in DRD1 and DRD2 neurons in order to reverse the effects of cocaine abuse.
Destabilizing Risks and Their Potential Effects on Iran’s 2024 Parliamentary Elections
Authors: Tan Olivia
Published: August 2024

This paper is a risk analysis examining threats to the stability of the Iranian state, the likelihood of further threat escalation, and their implications, done at the behest of the Islamic Labor Party for their consideration of potential campaign strategies in the upcoming 2024 parliamentary elections.
Monetary Flood Damage Prediction Based On Machine Learning Models
Authors: Bijoor Jaya , Raghavan Shreyaa
Published: August 2024

Flooding has increased by 50%, and for the past 25 years, the United States has experienced a flooding event every 2 to 3 days [1]. The Federal Emergency Management Agency (FEMA) is the only resource people utilize to determine whether their homes are at risk for flooding. FEMA maps are costly, outdated, and lack predictive accuracy, leaving homeowners unaware and unprepared for potential dangers [2]. Additional tools are needed to ascertain accurate estimates of flood damages, but computational methods require rich datasets, which are difficult to obtain for flooding events [3]. We create a model that predicts monetary damages using various factors related to the flood event, such as duration, location, and cause. We utilize a flood dataset from the National Oceanic and Atmospheric Administration (NOAA) and data pre-processing techniques to handle missing values and remove irrelevant features [4]. We generate aggregate predictions by leveraging linear regression, random forest regression, XGBoost regression, and neural networks. Our findings show we can effectively use upsampling techniques to combat limited flood data. We show that floods with higher monetary damages are easier to predict, which is important because these floods inflict greater hardship on communities. The results improve preparedness for flood-related risks, property value assessments, and the accuracy of insurance policy underwriting [2]. Ultimately, the model provides a preliminary study on how individuals can make better-informed decisions and prepare for the impact of flooding in their communities. We hope this work encourages further machine learning applications to help prepare citizens for natural disasters.
How Convenient are Artificial Intelligence and Machine Learning for Adoption among SMEs? Developing Countries Perspective
Authors: Ilankadhir M Tomasi Mutya
Published: August 2024

Artificial intelligence (AI) and machine learning (ML) technologies are crucial for small and medium enterprises (SMEs) to improve business operations in an easy, cost-efficient, and effective form. As a result, we explore the convenience dimensions that impact AI and ML adoption intention among SMEs in Uganda, a developing country. 248 respondents were involved. The results show that transaction, access, possession/post-possession, and search convenience impact SMEs’ intention to adopt AI and ML. However, evaluation convenience had no significant influence. Additionally, AI and ML adoption intentions among SMEs impact AI and ML adoption. Thus, our study contributes to AI and ML, convenience, SME literature, and practical applications for owners and managers of SMEs in developing countries.
An Enhanced Adaptive Medical Diagnostic Model Driven By Genetic–Neuro-Fuzzy Algorithms
Authors: O. Erihri Jonathan O. Ohwo Stephen , A. Adeyeye Emmanuel N. Okwor Anthony
Published: August 2024

This research aims at developing adaptive medical diagnostic system driven by genetic, neural network and fuzzy logic algorithms for effective diagnosis of tuberculosis. Medical diagnosis for tuberculosis disease is a complex decision process that involves a lot of vagueness and uncertainty management, since the disease has multiple symptoms. The use of several algorithms has been explored in clinical diagnosis models for diagnosing and prescribing therapy for tuberculosis, but has challenges in transforming the vagueness and uncertainty of multiple symptoms due to the noisy nature of the disease data and over fitting of the models, this had led to the challenge of accurate classification, training, optimization, diagnosis and prescription of therapy. Object Oriented Analysis and Design (OOAD) and System Structured Analysis and Design (SSAD) methodologies were adopted in the research. The methodology involved obtaining four hundred and thirty (430) clinical data from patients with tuberculosis records at the Tuberculosis and Leprosy Hospital, Eku, Delta State. The obtained data were pre-processed using missing values imputation and numeric data encoding methods. Thereafter, a genetic algorithm was applied to the processed data to select six relevant features from the initial number of 16 features (Cough, Night sweats, Fever, Systolic Blood Pressure, Difficulty in Breathing, Loss of appetite, Sputum, Chills, Loss of pleasure, Immune Suppression, Chest Pain, Lack of concentration, Irritation, Loss of energy, Lymph Node Enlargement, Body Mass Index). The reduced dataset was split into training and test sets. The ANFIS (Adaptive Neuro-Fuzzy Inference System) was applied to perform diagnosis in which it was trained and validated on training and test sets respectively. The ANFIS was driven by Mamdani’s inference mechanism with sixty-four (64) generated rules with confidence and support scores of above 10% and 15% respectively. The developed model performance on the test sets gave an accuracy of 90% precision, sensitivity, and specificity of 100%. The results showed that all the attributes of tuberculosis contributed to the degree of the diseases based on their respective weights. Conclusively, the system accurately classified, trained attributes, and predicts the severity of tuberculosis.
Strategic Planning and Management Practices on Quality Education Enhancement
Authors: Verano Vincent S. , Agrazamendez Doreen D. , Sarazain Koiser S. , Bauyot Marleonie
Published: August 2024

This study investigated the relationships between strategic planning, management practices, and quality education enhancement in basic education institutions. Data was collected from 200 public school teachers in Davao City using surveys on strategic planning, management practices, and performance capacities related to educational quality. Results showed high levels of strategic planning, with stakeholder perspective being the most manifested indicator. Management practices were also high, with designing a vision being most prominent. Quality education enhancement was high, with the school policy environment being the top indicator. A significant but weak relationship was found between strategic planning and quality enhancement, while management practices showed no significant relationship. Regression analysis indicated that strategic planning was a statistically significant predictor of quality education enhancement, albeit with a small effect size. Overall, the findings underscore the interplay of factors impacting educational quality improvement.