| 1 |
Author(s):
Tanishka Chougule .
Page No : 1-12
|
Cryptocurrency Regulation: MiCA v.s. USA Framework
Abstract
The objective of this study is to examine the cryptocurrency regulation system of the United States
with that of the European Union by highlighting consumer protection and industry development,
comparatively analyzing the two regulatory systems, and providing recommendations for next steps
for different types of consumers and companies. The comparison specifies the pros and cons of each
system and the trade-offs between legal certainty, consumer confidence, and economic competitiveness.
The study also identifies recommendations beneficial for varying corporations and individual consumers
through comparative case study. The analysis emphasizes that the stability of Markets in Crypto-Assets
Regulation (MiCA), a regulatory system utilised by the EU to supervise the cryptocurrency industry,
offers greater financial stability for consumers, while the U.S. model underscores legal rights for
corporations and flexibility. The conclusion is offered by outlining implications for firms, investors,
and consumers participating in the regimes of regulation, making recommendations for strategic
adjustment, and considers a possible scenario for global standardization.
| 2 |
Author(s):
Vir Sanghvi.
Page No : 13-27
|
Autonomous Weapons Systems: An Analytical Review of Technological Capabilities, Operational Challenges, and Mitigation Strategie
Abstract
While substantial research has explored individual components of autonomous weapons systems
(AWS), this study provides a comprehensive analysis comparing their capabilities, implementation
methods, and effectiveness factors across different systems and operational contexts. This systematic
literature review examines 49 studies from 2,847 initial records to investigate technological capabilities,
operational challenges, and mitigation strategies that define contemporary AWS programs. The analysis
shows that AWS capabilities have evolved from early automated systems to advanced platforms
incorporating artificial intelligence, cutting-edge materials, and network-centric integration. Three
categories of challenges primarily constrain the development of AWS: computational optimization
difficulties in weapon-target assignment systems, complexities in mechanical systems integration,
and limitations in materials science. The key findings suggest the effectiveness of virtual reality
training systems, which achieve over 80% accuracy in military applications; advanced multi-objective
evolutionary algorithms (MOEA/D-iAM2M) demonstrating superior convergence and diversity in
weapon-target assignment optimization; and aluminium-carbon nanotube composite materials with
enhanced ballistic protection capabilities. The findings also offer important insights for military
planners and defense contractors, highlighting the importance of focusing on systems that show
dependable integration and investing in communication capabilities to support network-centric warfare.
Moreover, the rapid advancements in artificial intelligence (AI), materials science, and manufacturing
technologies suggest an ongoing expansion of AWS capabilities, necessitating continuous research into
reliability, effectiveness, and responsible deployment.
| 3 |
Author(s):
Sourya Potti.
Page No : 28-39
|
Investigating Text-Guided Cross-Region Feature Alignment for Multimodal Disease Localization in Chest X-Ray Images
Abstract
Deep learning object detection techniques have been extensively applied to lung- and chest-related
healthcare applications. Recent advances in text-guided object detection techniques have led to substantial
performance improvements over image-based detection techniques. While such models employing
traditional region–text similarity have been explored for detecting abnormalities in chest X-rays, the
efficacy of models leveraging the concept of region-region similarity in this domain remains largely
unexamined. Although such architectures have demonstrated effectiveness in natural scene contexts,
their applicability to chest X-rays has been restricted due to the inherent challenges of the medical
object detection task. This gap prompts the question of whether chest X-ray-based disease detection can
be performed by training cross-region feature alignment architectures. In this study, this question is
addressed by systematically investigating a text-guided region-region similarity based object detection
architecture, dubbed CXR-CoDet. For this, this work investigates multiple training hyperparameter
configurations (with varying learning rate, batch size, number of training iterations), number of support
images needed for co-occurrence computation, different pretrained weights, different granularity of
disease descriptions, and incorporation of medical information through the text encoder. This work also
underscores the limitations of region-region similarity-based object detection architectures, particularly
applied in medical imaging, and provides recommendations for improvements. Code is available at:
https://github.com/souryatech/TGCRFA-CXR.git
| 4 |
Author(s):
Abhinav Sirigineedi.
Page No : 40-52
|
Exploring the Benefits and Risks Associated with Incorporating Artificial Intelligence into the Diagnosis, Treatment, and Monitoring of Alzheimer’s Disease
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of
dementia, posing a significant global health challenge due to the difficulties of early diagnosis and the
absence of a cure. This paper explores the transformative potential and associated risks of integrating
artificial intelligence (AI) into the diagnosis, treatment, and monitoring of AD. The main findings
indicate that AI tools, such as neuroimaging analysis and speech pattern recognition, enable significantly
earlier and more accurate detection, while also paving the way for personalized treatment plans and
remote patient monitoring. However, the review also concludes that these benefits are tempered by
substantial ethical and practical concerns, including data privacy, algorithmic bias, and the necessity for
human oversight, underscoring the need for responsible implementation.
| 5 |
Author(s):
Aneesh Dantuluri.
Page No : 53-60
|
Hybrid Supervised-Unsupervised CycleGAN for Virtual HER2 Immunohistochemistry from Hematoxylin and Eosin Stains
Abstract
HER2-status is a vital biomarker for breast cancer diagnosis and treatment, typically assessed using
immunohistochemistry (IHC), a technique that is expensive and demanding of laboratory experience.
Hematoxylin and eosin (H&E) staining, in contrast, is widely available and inexpensive, motivating
approaches that can computationally translate H&E images into IHC. While previous work has
explored translating H&E stains of breast tissue into IHC using purely unsupervised methods, this
study introduces a hybrid CycleGAN framework that combines unsupervised cycle-consistency with
supervised paired reconstruction objectives. By leveraging the paired structure of the BCI dataset,
this approach significantly improves quantitative metrics (PSNR: 16.203 → 17.807 (Adam); SSIM:
0.373 → 0.4061 (AdamW) and visual fidelity compared to unsupervised-only baselines, narrowing the
performance gap with supervised-only architectures while maintaining CycleGAN’s flexibility. These
f
indings show that incorporating limited supervision into cycle-consistent adversarial training enhances
H&E-to-IHC translation quality, offering a more affordable and accessible pathway to HER2 screening.
| 6 |
Author(s):
Stavyaa Girish.
Page No : 61-68
|
Anchoring Bias: The Effect of 52-Week Highs on Trading Volume in the S&P 500
Abstract
This paper investigates the presence of anchoring bias in stock markets through an analysis of
trading volume behavior at prices near the 52-week high. Anchoring bias, originally introduced by
Tversky and Kahneman as a cognitive heuristic, is defined as agents’ tendency to apply reference
points—usually arbitrary or externally generated—to make judgments in conditions of uncertainty. In
f
inancial markets, the 52-week high is a reference point that can influence the behavior of investors
more than fundamentals necessitate. Using the daily price and volume data of five S&P 500 leaders—
Apple, Microsoft, Alphabet, Amazon, and Nvidia—this research focuses on the calendar year 2024.
Unlike conventional definitions of the 52-week high, which may be based on intraday trades, this study
only used closing price data in the calculation, representing a methodological distinction from prior
work. Anchor days were defined by a price-based rule: a day on which the close was within 1% of the
then-existing rolling 52-week high. A two-tailed t-test was used to test whether mean trading volume
meaningfully differs on anchor and non-anchor days. By emphasizing trading volume rather than price
reactions, this paper introduces a novel behavioral measure of investor attention and conviction. The
f
indings hold broader implications for understanding how cognitive anchors shape liquidity, volatility,
and market efficiency.
| 7 |
Author(s):
Mia Moser-Gitter, David Yaron.
Page No : 69-90
|
Exploring the Transfer of Neural Network Potentials with Tight Binding Models of Conjugated Systems
Abstract
Neural network potentials (NNPs) have achieved impressive accuracy on local, short-range
chemical environments, yet their ability to capture long-range electronic effects remains unclear. We
investigate this question using a controlled testbed for electron delocalization: 1D tight-binding chains
with 2–10 atoms. We generated synthetic datasets by drawing nearest-neighbor coupling values from
a narrow normal distribution and determining the resulting ground-state energies using Hamiltonian
diagonalization. We benchmark two architectures: (1) a “full chain” feed-forward MLP that ingests
the entire coupling vector and directly predicts the total energy, and (2) a Behler–Parrinello–style
“atom environment” model that sums per-atom energies from local windows (sizes 1–3). Across chain
lengths, the full-chain model consistently attains lower error and cleaner predicted-vs-true alignments,
though accuracy degrades with system size for all models. For 10-atom chains, mean errors are 0.0279
(full-chain) versus 0.0613, 0.0524, and 0.0417 for window sizes 1, 2, and 3, respectively. Loss curves
show stable optimization with limited overfitting on small and mid-sized systems. These results
indicate that even simple global-feature MLPs can learn delocalization effects that challenge purely
local descriptors. Key limitations include the idealized 1D tight-binding ground truth, nearest-neighbor
couplings only, and the absence of symmetry/equivariance constraints, which limit transferability to
realistic 3D chemistry. Nonetheless, the findings motivate combining global receptive fields (or longer
range descriptors) with physics-aware architectures to improve generalization in delocalized electronic
systems.
| 8 |
Author(s):
Allyson Luo.
Page No : 91-100
|
Dancing for Dopamine: A Review on the Efficacy of Dance in the Treatment of Parkinson’s Disease
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s
in the United States. It is a progressive movement disorder that affects the substantia nigra, an area of
the brain containing neurons that are responsible for producing dopamine. Symptoms of PD include
tremors, rigidity, bradykinesia, cognitive impairments, and mental challenges. While there is currently
no cure for PD, there are several pharmacological treatments that aim to alleviate the symptoms. These
treatments, however, often cannot address the full breadth of the complex disease. As a result, increasing
research has been conducted on several non-pharmacological interventions, including music therapy,
community-based therapy, and exercise. These modalities have been shown to improve motor and non
motor symptoms and slow disease progression. Dance has emerged as a promising non-pharmacological
intervention for PD. Several studies suggest that dance may be more beneficial to people with PD than
music therapy, community-based therapy, and exercise alone. Dance synergistically combines music,
community, and exercise, which may explain why dance is such an effective treatment.
| 9 |
Author(s):
Arnav Bansal.
Page No : 101-108
|
Enhancing Pollen Allergy Severity Predictions Through Machine Learning
Abstract
Pollen allergies significantly impact global health, with rising prevalence and severity exacerbated by
climate change. These conditions reduce quality of life and increase healthcare costs. Traditional pollen
monitoring techniques are slow, labor-intensive, costly, and lack timely, location-specific accuracy,
while general forecasts are often unreliable. This research develops a real-time prediction model for
pollen allergy severity using environmental and meteorological data combined with advanced machine
learning methods. Four models were evaluated: a baseline Random Forest, XGBoost, tuned Random
Forest with time series cross-validation, and an advanced Random Forest incorporating cyclical date
features, lagged pollen values, and rolling averages. The final model achieved the best performance
with an R² value of 0.78. Significantly surpassing the approximately 50% accuracy typically achieved
by prior forecasts. Results demonstrate that integrating environmental and seasonal features can
substantially enhance the accuracy of pollen allergy severity predictions. Future work should aim to
improve model generalizability across diverse regions and to expand the availability and temporal
resolution of training data.
| 10 |
Author(s):
Sonoma Peterson.
Page No : 109-117
|
The Factors Leading to Reduced Sexual Dimorphism in H. floresiensis and H. naledi
Abstract
Homo naledi (H. naledi) and Homo floresiensis (H. floresiensis) are hominin species that both display
reduced sexual dimorphism. The factors that cause this reduction are debated. For H. naledi, two factors
are debated as the primary causes. Those factors include the emergence of new tools, which make
hunting easier, and pair bonding, which reduces male competition, leading to postponed adolescence
and smaller canines and body size in males. Their mix of ancient and derived anatomical traits also
makes it difficult for researchers to differentiate between the sexes. As for H. floresiensis, the island rule
suggests that fewer resources result in a smaller overall body size for the species. The smaller overall
body size increases pair bonding to fend off predators, which means less male competition, making
things like larger canines and bigger body size a waste of energy. This review finds that while there
is evidence supporting the theories of pair bonding, insular evolution, and reduced male competition,
the current framework used for sex determination requires additional evidence to draw a definitive
conclusion, and the available data remain too limited to distinguish whether these reductions reflect
adaptive social strategies or developmental constraints.
| 11 |
Author(s):
Sidharth Maheshwari .
Page No : 118-131
|
Comfort-Aware Motion Planning for Robot-Assisted Feeding
Abstract
Robot-assisted feeding systems offer significant promise for individuals with partial motor
impairment, yet delivering food comfortably to a human mouth remains a complex challenge. This
work introduces a novel approach to motion planning in assistive feeding settings which both models
and optimizes for human comfort. A comfort cost function was developed that integrates trajectory
smoothness, velocity, and a novel “closeness” metric grounded in proxemics. This cost function serves
as a guide to a new motion planning sampling strategy and a cost-aware shortcut smoothing for post
processing. Through simulation experiments in PyBullet across various realistic assistive feeding
scenarios, the proposed methods generated paths with 17% decrease in comfort cost compared to
standard planners making it more suited for adapting to human comfort. While the approach shows
substantial gains in environments with a wider free configuration space, improvements were limited
in environments where the free configuration space was narrower. These findings suggest a promising
foundation for comfort-aware motion planning, with implications for improving autonomy and
independence in robotic caregiving.
| 12 |
Author(s):
Johnson Ye.
Page No : 132-142
|
Comparison of Solar Energy Storage Methods and Their Implications on Integration with Renewable Energy
Abstract
Decarbonizing the electrical grid through large-scale implementation of solar energy can address
both climate change concerns and the growing global energy demand. While solar energy is abundant,
effective storage remains a major challenge due to environmental and integration constraints. If solar
energy can be efficiently stored on a large scale, it could provide a sustainable solution to humanity’s
energy and climate crisis. This article systematically compares six major solar energy storage methods,
lithium-ion batteries, redox flow batteries, compressed air energy storage, thermal energy storage,
hydrogen energy storage, and pumped-hydro energy storage, to determine which is most suitable
for large-scale integration with solar energy systems. For each method, the principles, advantages,
disadvantages, and future potential are analyzed to evaluate their feasibility for global application.
Pumped-hydro energy storage is shown to be the most promising among the methods discussed.
| 13 |
Author(s):
Angela Liu.
Page No : 143-152
|
Clinical Applications and Challenges of Personalized Medicine in Cardiovascular Disease
Abstract
Personalized medicine is an approach to healthcare that tailors treatment strategies to a patient’s
genetic makeup. It can be applied to the prevention, diagnosis, and treatment of cardiovascular
disease (CVD). Its integration into cardiovascular disease management is particularly important, as
CVD remains one of the leading causes of death worldwide. Through pharmacogenomics, healthcare
providers can identify early indicators of CVDs, diagnose specific CVDs, and individualize therapies
for patients. However, privacy risks complicate the implementation of personalized medicine, as the
collection and storage of genetic information can potentially expose patients to misuse, security risks,
and genetic discrimination. Ethical concerns about healthcare equity also arise, due to high costs and
lack of genetic research on diverse populations. Legal protections such as the Genetic Information
Nondiscrimination Act highlight progress in navigating issues related to personalized medicine, but
future measures are necessary to support pharmacogenomics’ wider implementation. This review
analyzes the applications of personalized medicine in cardiovascular care and addresses the privacy and
ethical challenges that accompany pharmacogenomic advances.
| 14 |
Author(s):
Anvitha Karnati.
Page No : 153-163
|
From Justification to Fairness: Reframing the Basis of Democratic Legitimacy
Abstract
This paper examines whether the legitimacy of democratic policy should not rest on citizens’ ability
to publicly justify their post-deliberation positions to those who disagree. While deliberative theorists
position justification as the foundation for legitimacy, this paper asserts that justification is neither
feasible nor desirable as the base. Using conceptual analysis, I examine the requirements of reciprocity,
mutual respect, and publicity in democratic theory and demonstrate how justificatory frameworks
systematically exclude certain voices and produce exclusion. The result of this analysis is that fairness,
rather than consensus-based justification, should serve as the foundation for democratic legitimacy.
This paper concludes with the argument that structuring deliberative democracy around fairness better
secures legitimacy in pluralistic democracies by preserving mutual respect without enforcing consensus.
| 15 |
Author(s):
Shashank Kailash Muthuraju.
Page No : 164-170
|
Role of Zinc and Shank Protein Family in Autism Spectrum Disorder
Abstract
Autism spectrum disorder (ASD) is a brain developmental disorder influenced by various genetic
and environmental factors. Zinc, a trace element essential for cellular functions such as growth, wound
healing, and immune support, also plays a novel role in brain development during pregnancy. During
brain development, synoptic proteins have a major role in successful brain development. Shank is one of
the synaptic proteins which are regulated by Zinc. Zinc and Shank are mostly involved in neurological
disorders, especially in Autism. Much recent literature highlights the involvement of Zinc and Shank
in neurological disorders, particularly ASD. In PubMed, there are nearly 250 articles published in
association with Zinc and Autism spectrum disorder. Similarly, the zinc and shanks protein family are
widely studied and there are 125 articles published in the ASD model. However, the crosstalk between
zinc and shanks proteins has not been exclusively reviewed in the development of autism since last
year. The purpose of this review is to highlight the fundamental role of zinc and shank proteins in the
development of autism. The first part of the review focuses on the role of Zinc and Shank in the ASD
brain, and the second part focuses on crosstalk between zinc and shank genes in the brain especially in
ASD.
| 16 |
Author(s):
Andrew Kim.
Page No : 171-176
|
Exploring the Benefits of Solving the Rubik’s Cube for Older Adults
Abstract
With a growing aging population worldwide, it is important to combat age-related cognitive
impairment, emotional loneliness, and reduced motor skills. This research investigates the cognitive,
emotional, and motor gains of playing the Rubik’s Cube among older people. Using a quasi-experimental
mixed-methods design, data were collected using cognitive testing, dexterity testing, rating scales for
mood, and participant observations over a month. Improved cognition was evident for 33%, improved
dexterity for 78%, and improved mood for the overall group. On average, participants took an
equal amount of time on traditional puzzle play compared with Rubik’s Cube. Important themes for
qualitative answers included improved concentration, motivation, and emotional fulfillment. Due to a
limited sample size and lack of a comparison group, the study shows promise for the Rubik’s Cube as
an effective, low-cost intervention for healthy aging and neuroplasticity for older individuals.
| 17 |
Author(s):
Desika Shyam Sundar.
Page No : 177-182
|
Effects of Fragmented REM Sleep on Emotional Consolidation in Individuals with Nightmare Disorder
Abstract
Nightmares, especially when frequent, can significantly disrupt the brain’s process of consolidating
emotional memories during rapid eye movement (REM) sleep. REM provides a unique neurobiological
environment for integrating emotionally charged experiences, but nightmares fragment or shorten this
stage, impairing emotional regulation and memory clarity. This literature review synthesizes findings
from neuroscience, sleep medicine, and psychology, showing that recurrent nightmares can reinforce
threat-related associations and hinder adaptive emotional processing. These insights highlight the
potential of targeted interventions, such as imagery rehearsal therapy and memory reactivation, to
restore healthy REM function and improve emotional resilience.
| 18 |
Author(s):
Seohyun Hong.
Page No : 183-191
|
A Cross-Country Analysis on Artificial Intelligence Diffusion, Energy Intensity, and Income
Abstract
This study sought to examine how national diffusion of artificial intelligence measured by the share
of artificial intelligence job postings is related to energy intensity and income. The hypothesis in this
study was that the greater the diffusion of artificial intelligence is, the lower energy intensity is, reflecting
efficiency gains, and higher income, reflecting productivity gains. Using two publicly available datasets,
this study, this study merged country-year records, analyzing the most recent year with broad coverage
in cross-sectional design. Outcomes identified energy use per unit of economic output and income per
person by applying artificial job posting share as a single explanatory variable. Models were estimated
with bivariate ordinary lease squares for the purpose of replication. Results indicated that there was
a positive but statistically inconclusive association between the diffusion of artificial intelligence and
primary-energy intensity. In addition, when focusing on electricity intensity, there was a small and
statistically null association, while there was a positive and precise association between the diffusion of
artificial intelligence and income as confirmed by three light robustness checks, including a logarithmic
specification and a leave-one-out influence analysis. The findings in this study were not casual but
descriptive. Future work is recommended to focus on extending to a short panel with fixed effects,
emphasizing outcomes specific to electricity and carbon to add readily available controls and examine
heterogeneity by region or income group.
| 19 |
Author(s):
Tianhui Wang.
Page No : 192-207
|
Polyethylene Terephthalate Induced Oxidative Stress in Chlamydomonas reinhardtii: Implications for Intracellular Response Pathways and Ecosystem Health
Abstract
Polyethylene terephthalate (PET) is a cheap and versatile plastic used primarily in textiles. We
dispose of it as solid waste and in wastewater. It degrades into microplastics and decomposes into
small molecules. This review explores recent studies on the impact on green algae of PET microplastic
pollution in freshwater and soil. Using the example of the unicellular alga Chlamydomonas reinhardtii,
a model organism for photosynthetic eukaryotes. The analysis explores mechanisms and outcomes of
reactive‑oxygen‑species (ROS) generation, lipid peroxidation, and disrupted electron transport leading
to impaired photosynthesis. Although most prior studies have targeted PET additives or dyes, evidence
increasingly suggests that the PET polymer and its monomeric fragment, terephthalic acid, are not
inert but ecotoxic. Proposed pathways link PET photodegradation and metal‑chelation chemistry to
benzoquinone formation and chronic cellular oxidative stress. Yet existing data remain fragmented:
few studies address decomposition kinetics, ion binding, or ecosystemic feedback in complex natural
conditions. The article concludes that advancing quantitative, system‑level models of PET impact—
and implementing stricter controls on PET dispersal through wastewater and biosolids—is essential to
mitigate growing biospheric and economic risk.
| 20 |
Author(s):
Christopher Ann, Ryan J. Farber.
Page No : 208-217
|
The Significance of Metallicity in Determining Stellar Mass
Abstract
Since Joseph von Fraunhofer’s 1814 discovery of solar absorption lines, spectroscopy has become
central to understanding stellar composition and the process of star formation. Building on the
foundational contributions of Kirchhoff, Bunsen, and Payne-Gaposchkin, modern astrophysics has
examined metallicity (the abundance of elements heavier than hydrogen and helium) as a key factor in
star formation processes and the Initial Mass Function (IMF). Observational surveys and simulations
suggest that metallicity influences stellar fragmentation, cooling, and feedback, with lower metallicities
often favoring more massive stars. The mass-metallicity relation (MZR) and its evolution further
underscore how galaxy growth, star formation rates, and feedback mechanisms influence metallicity
trends over time. However, the role of metallicity remains complex and contested, with turbulence,
accretion history, and environmental factors playing equally significant roles. The case of Population
III stars (formed in metal-free environments) demonstrates that stellar mass can arise independently
of metallicity, emphasizing the limits of a universal link between metallicity and IMF. This review
synthesizes key observational evidence, theoretical models, and limitations, concluding that metallicity
is a central but non-exclusive factor in determining stellar mass. Future progress will rely on integrating
high-resolution simulations, advanced stellar population models, and next-generation observations,
particularly from JWST, to clarify the exact relationship between metallicity and other environmental
drivers of star formation.
| 21 |
Author(s):
Yashica Mahajan.
Page No : 218-222
|
The Impact of Artificial Intelligence Advancements on the Frequency and Severity of Flash Crashes in Financial Markets
Abstract
Flash crashes, sudden, severe market price dislocations followed by rapid recovery, reflect the
growing influence of automation in financial trading. Since the 2010 event that erased nearly one trillion
dollars in value within minutes, markets have become increasingly dependent on algorithmic and
artificial intelligence (AI)driven systems. This narrative review examines how recent AI advancements,
including machine-learning–based prediction and autonomous execution models, influence the
frequency and severity of flash crashes. Evidence from empirical studies indicates that AI improves
trading speed, liquidity, and efficiency but simultaneously amplifies systemic vulnerabilities through
algorithmic clustering, herding effects, and opacity in decision-making. While regulatory mechanisms
such as circuit breakers and the SEC’s Limit-Up/Limit-Down Plan mitigate immediate volatility,
persistent challenges remain in transparency, governance, and ethical oversight. The review concludes
that AI’s ultimate effect on financial stability depends on the balance between innovation and regulation,
whether algorithmic intelligence becomes a stabilizing safeguard or a catalyst for new forms of market
disruption.
| 22 |
Author(s):
Shaina Gupta.
Page No : 223-233
|
Evaluating Generative AI for Startups: A Benchmarking Study of Large Language Models
Abstract
Startups are critical for global economic development, and they are often constrained by resources
and affiliations, limiting their growth, unlike larger companies. Concerningly, nearly 70% of startups
fail 2-5 years after their launch. The way to eliminate this discrepancy is to leverage commercial, readily
available AI tools to assist with tedious tasks, so human capital and financial resources could be better
spent on business development. This paper intends to use prompt engineering principles and evaluation
rubrics that assess appropriate AI tools for different necessities of startups to answer the following
question: How do different large language models (LLMs) vary in their prompt responses in AI-driven
solutions for startups, and what implications does this have for selecting generative AI tools in small
business contexts? An exclusive public dataset of prompts and commercial-LLM responses is being
released that can be used by startups to evaluate the effectiveness of integrating AI tools for specific
business activities. This dataset can be leveraged by startups of all types, to baseline the selection of AI
tools, allowing them to allocate resources to more meaningful aspects of a business. This is being done
for three business cases, including web design, market research, and business support. Each of these
business cases have several prompts which are evaluated with 2-3 different LLMs to determine the
optimal LLMs for different use cases. The key findings were that for certain use cases like web design,
general usage LLMs like ChatGPT 5.0 produced optimal results, but in contrast, for other use cases like
market research and business analysis, the specialized LLMs that provided lots of research performed
better, like Claude. Therefore, the quality of results based on LLMs is on a case by case basis, but it can
be extrapolated to the majority of prompts under the jurisdictions of those three use cases.
| 23 |
Author(s):
Edward Lee.
Page No : 234-243
|
Factors Shaping U.S. Public Attitudes Toward Refugees: Political Orientation, Historical Acceptance, and Perceived Threats
Abstract
This study examines the factors that influence public attitudes in the United States toward refugees,
focusing on political orientation, past acceptance, and perceived threats. Understanding these dynamics
is essential because public opinion shapes not only immigration and resettlement policy but also the
social context in which refugees are received. Data were collected through questionnaires from 106
U.S. residents, measuring demographic traits, political ideology, local histories of refugee acceptance,
perceived threats (economic, cultural, security, and infrastructure), and attitudinal support for refugees.
Multiple regression analyses (R² = 0.593) revealed that security-related concerns (b = -0.53, p < 0.001)
and public service strain (b = -0.33, p < 0.01) were the significant negative predictors of refugee
support, while historical acceptance (b = 0.44, p < 0.001) predicted more positive attitudes. Political
conservatism also significantly predicted negative attitudes. These results indicate that safety and
infrastructure concerns outweigh economic or cultural fears and that communities with past refugee
resettlement display greater local support. The findings offer both scholarly and policy relevance,
providing a foundation for targeted strategies that address key public concerns while strengthening
community-based acceptance of refugees.
| 24 |
Author(s):
Yufei Lu.
Page No : 244-253
|
Univariate versus Joint Modeling of Interest Rates and Inflation
Abstract
This study investigates several forecasting approaches for the United States federal funds rate and
consumer price index (CPI) inflation rates in the years 1980-2025. This study compares univariate
and joint models under trend and autoregressive specifications, implementing each model in
connection with a historical data set and a three-year holdout test sample (2023-2025). The primary
emphasis is discovering which approach to forecasting will provide greater predictive accuracy and
relevance for macroeconomic policy purposes. The results of empirical work show that while these
trend models serve well in capturing long-term trends in monetary and price behavior, autoregressive
models perform better in forecasting short-term values because of their greater flexibility in taking
into account variations and external factors. Joint modeling of interest rates and inflation through a
vector-autoregressive (VAR) system provides small yet significant increases in forecasting accuracy
but demonstrates that intermingling between monetary policy and price stability improves forecast
accuracy. These findings suggest that accuracy in forecasting is not purely a statistical problem but
a crucial policy problem which illustrates that even slight improvements can avoid premature policy
tightening or delayed economic easing. Thus, this study seeks to indicate the importance of integrating
dynamic, feedback-giving modeling systems into central bank databases in order to bring greater data
usefulness and greater stability in financial markets.
| 25 |
Author(s):
Zoey A Lee.
Page No : 254-266
|
The Genetics of Sporadic Alzheimer’s Disease and the Potential of CRISPR and Exosome-Based Therapies
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the
accumulation of beta-amyloid plaques and hyperphosphorylated tau tangles. It can be classified as
either familial or sporadic, with sporadic AD being the most common. While each type has its own
genetic associations, the apolipoprotein E (APOE) gene and its variants are the most strongly linked
with sporadic Alzheimer’s. Given the genetic component of AD, CRISPR/Cas9 technology has recently
emerged as a promising tool for genetic therapy due to its precision and efficiency. However, its clinical
translation faces significant challenges, particularly in achieving safe and effective delivery to the brain,
which requires penetration of the blood-brain barrier (BBB). Exosomes, small extracellular vesicles
capable of naturally crossing the BBB, offer a potential solution for CRISPR/Cas delivery. Advances
in engineering have led to the development of “designer exosomes,” which can be modified to enhance
stability, targeting and cargo capacity. This review covers key genetic risk factors associated with
sporadic Alzheimer’s and explores how CRISPR/Cas systems, together with exosome-based delivery,
have the potential to be applied for therapeutic and diagnostic purposes in AD. By synthesizing recent
studies, this review highlights that combination of CRISPR/Cas with engineered exosomes represents a
promising strategy for future AD research and therapy.
| 26 |
Author(s):
Zixian Yang.
Page No : 267-274
|
Quantifying Blood Alcohol Concentration (BAC) Effects on Driver Behavior, Collision Manner, and Injury Severity: An Observational Analysis with Demographic Predictors
Abstract
This study explores driver behavior through correlational relationships between different variables and crash-related factors, particularly in the form of alcohol use. Though alcohol consumption being a risk factor contributing to car crashes had already been the focal point of previous studies (Hingson and Winter, 2003, Shyhalla, 2014), its amount and impact on different aspects of crashes remains relatively unexplored. Using crash data from the Fatality Analysis Reporting System (FARS) published by the National Highway Traffic Safety Administration, three individual models (one logistic regression and two multinomial logistic regressions) were developed to analyze the effects of alcohol consumption and other control variables on an individual’s seatbelt usage, injury severity sustained, and collision type experienced. It was determined that alcohol usage had substantial effects on crash-related factors by significantly reducing seatbelt usage, increasing the likelihood of severe injury, and had some influence on shaping the type of collision experienced. Other variables—age and sex—played a smaller role in influencing these factors. This research reinforced the dangers of alcohol usage through not only strengthening its identity as a risk factor to crashing, but expanding on its impacts on other components as well, stressing the importance of shaping drivers to operate vehicles alcohol-free.
| 27 |
Author(s):
Daniel Park.
Page No : 275-282
|
The Perpetual Struggle for Access Within Reformed Veterans’ Health Systems
Abstract
Veteran healthcare in America exposes the disparity between policy change and lived experience.
Though promising legislation like the Veterans Access, Choice, and Accountability Act (2014), VA
MISSION Act (2018), and Honoring Our PACT Act (2022) were proposed, implementation of these
reforms has been inconsistent. This paper examines the structural, logistical, and social barriers that
still complicate providing equitable care to millions of veterans. Despite the Department of Veterans
Affairs (VA) operating one of the largest healthcare systems in the United States, veteran patients—
especially those in rural areas, minorities, or those with complex mental health or disability needs—
often experience delayed appointment access as increased unidentified staff shortages plague care
coordination across care networks. The Government Accountability Office has drawn attention to
numerous inconsistencies and deficiencies in these facets identified in oversight reports. The inclusion of
geographic and racial inequities alongside systemic ambiguity when federally funded programs overlap
demonstrates how these issues significantly complicate access to care. These reports have exposed the
consequences of missed and inconsistent treatment. Although reforms have increased eligibility and
somewhat improved treatment outcomes, they have equally worsened disparities within an already
under-resourced system. This paper argues that true progress is not solely through policy but through
aligning legislative ambition with effective implementation: attention to recruitment of providers,
enforcement of oversight, and the establishment of a culturally competent veteran-centered healthcare
system to help veterans serve as advocates for themselves. Ultimately, bridging the gap between policy
and practice will deliver the healthcare our nation’s veterans deserve.
| 28 |
Author(s):
Zonghan Jia.
Page No : 283-289
|
A MANOVA & ANOVA Based Investigation of Vehicle Class Differences in Occupant Injury Metrics
Abstract
This study investigates the influence of vehicle type on crash-test injury outcomes by analyzing
multiple biomechanical metrics, including head injury criteria (HIC), chest deflection, femur loads,
neck injury measures, rib deflection, abdominal force, and pelvic force, for both drivers and passengers.
A multivariate analysis of variance (MANOVA) followed by ANOVA tests examined that vehicle type
has a statistically significant effect across basically all the injury merits (Wilks’ Λ = 0.21, F(57,13683)
= 48.72, p < 0.001). The strongest differences were observed in side-impact head injury (HIC36),
abdominal force, and rib deflection. Passenger cars exhibited higher torso-related injury measures,
vans showed elevated neck injury risks, and trucks demonstrated comparatively lower torso injuries
but increased femur forces. These findings indicate that no single vehicle category provides uniform
protection from all body regions. The study recommends that consumer safety regulators consider
differentiated crash standards by vehicle type and that consumer safety ratings explicitly communicate
the injury trade-offs that exist across different vehicle classes.
| 29 |
Author(s):
Wenxi Weng.
Page No : 290-299
|
Aspartame Consumption and Gastrointestinal Health: A Critical Review of Recent Findings
Abstract
Aspartame is a widely used non-nutritive sweetener approved for use in food for decades. Although
multiple reviews have examined artificial sweeteners and metabolism, few have specifically focused on
gastrointestinal mechanisms and microbiota responses. Potential gastrointestinal effects of aspartame
consumption remain unclear and sometimes contradictory. This review uniquely synthesizes recent
evidence (2011–2025) to clarify both adverse and potentially beneficial effects of aspartame on gut health,
with a few earlier studies included as background evidence. Current studies indicate that aspartame
alters gut microbiota, with downstream consequences including metabolic dysfunction, irritable bowel
syndrome, and possible anti-inflammatory effects. Some studies report that aspartame may compromise
epithelial integrity under certain experimental conditions, although more recent evidence has shown no
clear association. Meta-analyses suggest potential protective effects, while concerns persist regarding
carcinogenic byproducts such as formaldehyde. Given heterogeneity across studies and reliance on
animal or in vitro models, definitive conclusions cannot be drawn. This review highlights the need for
standardized human studies to resolve inconsistencies, clarify dose-dependent and long-term outcomes
of aspartame consumption, and guide dietary recommendations.
| 30 |
Author(s):
Theodora Papageorgiou .
Page No : 300-311
|
How Exposure To Violent Media Impacts Aggression In Children: Exploration Of Gender Differences
Abstract
With the rapid evolution of social media, children are spending more time on screens than ever
before. Given this increased screen time, there is growing concern about violent media because such
exposure can result in normalized aggressive behavior. Exposure to violent content and its effects
on childhood aggressive behavior are examined, with particular focus on the role of gender and the
specific context of Greece. Data was collected through semi-structured interviews with 12 mothers of
school children living in Athens, Greece. Most mothers perceived that both girls and boys exposed to
media violence expressed some form of aggression and/or violent thoughts. However, girls expressed
a different kind of aggression than boys, as a result of their different content preferences. Specifically,
girls express verbal violence, whereas boys resort to physical violence. Future studies should examine
whether the gender differences in expressing violence follow children into their adult lives.
| 31 |
Author(s):
Omer Alp Daniş.
Page No : 312-323
|
Random Walks, Electrical Networks and Pólya’s Theorem
Abstract
A fundamental topic in probability theory is random walks on infinite graphs. In particular,
classifying them as recurrent and transient is of utmost importance for the understanding of their
behavior. These properties reveal deep connections to electrical networks and highlight the structural
properties of the underlying space. Approximately, a random walk is recurrent if it cannot escape to
infinity; otherwise, it is transient. Pólya’s Theorem characterizes recurrence in dimensions
Zd
f
or d
= 1,2 and transience for d ≥ 3. Building on this theorem, the paper examines how the addition or
deletion of edges impacts effective resistance to infinity and, consequently, the recurrence-transience
classification. Using the analogy between random walks and electrical networks developed in Lyons
and Peres, the paper interprets recurrence as infinite effective resistance and transience as finite
resistance to infinity. Tools such as the Nash-Williams Criterion, Rayleigh’s Monotonicity Principle,
and Thomson’s Principle are used to analyze resistance under graph modifications. The study confirms
Pólya’s Theorem and explains the dimensional threshold: in low dimensions, limited connectivity
causes resistance to diverge, ensuring recurrence; in higher dimensions, the abundance of disjoint paths
support finite-energy flows, leading to transience. The paper also investigates how adding or deleting
edges alters resistance profiles. It is shown that while finite changes typically preserve recurrence
or transience, systematic or unbounded modifications can switch the behavior entirely. In short, by
combining ideas from probability and electrical network theory, the paper provides insight into how
random walks behave in different dimensions and how changes like adding or removing edges can
influence that behavior.
| 32 |
Author(s):
Yusuf Efe Kızılöz.
Page No : 324-335
|
How Do Endowment and Disposition Effects Influence Portfolio Choices and Challenge the Assumptions of the Efficient Market Hypothesis?
Abstract
This paper examines how two behavioral biases, the endowment effect and the disposition effect,
shape portfolio choices and challenge the assumptions of the Efficient Market Hypothesis (EMH).
EMH assumes that asset prices completely incorporate all information available and that investors
are rational in their behavior. Yet findings in behavioral finance on these two effects demonstrates
systematic deviations from rationality. The disposition effect captures investors’ tendency to sell
winning assets too quickly while irrationally holding losing ones; the endowment effect describes
individuals’ assigning greater value to assets they already own than to identical assets they do not.
These are the principal sources of bias examined in this paper. Far from random, these behaviors
repeat themselves systematically across markets, investor groups, and time periods, thereby weakening
EMH’s methodological assumption that countervailing irrationalities cancel one another. They reduce
diversification, delay portfolio rebalancing, and distort the intended risk–return trade-off, pushing
investors away from the efficient frontier. Their persistence also constrains the EMH assumption
that arbitrage always corrects mispricings, since real markets face transaction costs and structural
frictions that prevent full adjustment. By showing that psychological factors—such as reluctance to
realize losses, reference dependence, and ownership-driven attachment—steer investment decisions,
this study argues that classical financial models must be extended. In brief, while EMH is a helpful
theoretical benchmark, it cannot fully account for market action when psychology-based biases exist. To
understand how investors choose, how portfolios evolve, and how markets actually function, behavioral
perspectives must be integrated.
| 33 |
Author(s):
William Kim.
Page No : 336-344
|
Predicting the Vaccine-Safety Misinformation Spread Using Logistic Diffusion Modeling: A Public-Health Modeling Study
Abstract
Vaccine-safety-related misinformation continues to hinder public confidence, while slowing
immunization efforts worldwide. It is crucial to understand how such misinformation spreads through
social media to help public health agencies respond to it more efficiently. This study developed and
applied a logistic diffusion model to predict the rise, peak, and decline of vaccine-related misinformation
by using open-access datasets named the COVID-19 Healthcare Misinformation Dataset (CoAID) that
verified false vaccine-related claims and their online circulation were cataloged. Specifically, five claims
of vaccine-safety misinformation were extracted from the CoAID dataset for analysis. This study aimed
to determine whether logistic diffusion modeling of open-access social media data may accurately
predict the pread of misinformation related to vaccine-safety. This study hypothesized that a self
limiting logistic pattern characterized by rapid early diffusion and eventual saturation may be followed
with misinformation as shown in epidemic processes. Five representative claims of vaccine-safety were
chosen and analyzed by using spreadsheet-based curve fitting to fulfill transparency and reproducibility.
All claims indicated the expected S-shaped diffusion pattern, ensuring that misinformation spread was
predictable and bounded at the same time. Faster-spreading misinformation turned out to peak sooner
and faded quickly, while slower-spreading misinformation persisted longer and reached a broader
pool of audiences. This approach offered an accessible and data-driven means to inform the timing of
counter-messaging strategies as a public-health communication effort in the future. The mean growth
rate (r ≈ 0.29) and model fit (R2 > 0.95) confirmed the high predictive accuracy of the logistic model.
| 34 |
Author(s):
Roland Pratt, Dr. Ryan Farber.
Page No : 345-352
|
Integrating Star Fixes into Multi-Drone Localization Frameworks
Abstract
Celestial navigation has recently matured into a viable backup for Unmanned Aerial Vehicle
positioning, while collaborative localization frameworks have shown the benefits of sharing relative and
absolute measurements across swarms. Yet, integrating star-based fixes into multi-drone architectures
remains largely unstudied. This review provides an overview of work on single-platform celestial
sensing, cooperative localization, and analogous leader–follower systems, and synthesizes the results
to predict that sparse, asynchronous celestial star fixes, injected as absolute factors into a distributed
collaborative estimator and scheduled by sky-aware planners, could achieve sub-kilometer accuracy
over day-long Global Navigation Satellite Systems (GNSS) outages and surpass the capability of any
single drone. To achieve this, swarm-level celestial fusion could exploit spatial diversity, temporal
compression, and drift-limiting loop closures. Key research gaps remain in confirming this prediction
with experimental testing, optimal sensor distribution, the balance between inter-drone and celestial
measurement noise, and ensuring sky visibility through information-aware planning. Addressing these
challenges is critical for building resilient positioning systems in GNSS-denied environments.
| 35 |
Author(s):
Nathan Niklewski.
Page No : 353-363
|
Identifying Quantum Teleportation Error Mechanisms Through Bitstring-Level Noise Signatures
Abstract
Identifying dominant noise mechanisms is a central challenge in today’s noisy quantum devices,
where every operation is affected by hardware imperfections. To investigate how noise manifests in
quantum operations, this study employed a three-qubit teleportation circuit as an experimental probe.
Ideal simulations, noisy simulations in Qiskit Aer (readout misassignment, depolarizing, biased
Pauli, thermal relaxation), and runs on IBM Brisbane were compared after applying standard X and
Z corrections in post-processing. Distinct patterns in the bitstring distributions showed that IBM
hardware is dominated by readout-driven effects with mild bias in Pauli error probabilities. A short
teleportation “probe” thus provides a simple, circuit-specific diagnostic test to guide noise mitigation.
This study aims to determine whether bitstring distributions can serve as fingerprints for identifying
dominant noise sources in quantum teleportation circuits.
| 36 |
Author(s):
Yiyang Zheng.
Page No : 364-369
|
Blue Straggler Star Formation in the Field Regions of M31
Abstract
During stellar evolution, some stars evolve into blue straggler stars (BSS), stars that are more blue
and luminous compared to the main sequence turnoff point (MSTO) of the parent generation. Two
main theories for BSS formation exist, stellar collision and binary mass transfer. One way to determine
which theory led to BSS formation is by determining if there is a correlation between local stellar
density and BSS frequency. A strong positive correlation suggests that collision is the main method,
while a very weak or negative correlation suggests that binary mass transfer dominates BSS formation.
While most previous research have focused on BSS formation within stellar clusters, this paper intends
to find a trend between local stellar density and binary mass transfer in the field regions of a large spiral
galaxy, Andromeda (M31). Data from the Panchromatic Hubble Andromeda Treasury (PHAT) was
used, and BSS candidates were identified. Local stellar density was then plotted against BSS frequency
for each star in a logistic regression, and the coefficient was recorded. Out of 11 fields, 8 fields showed a
weak trend with a confidence interval containing zero for the coefficient, with coefficients ranging from -2.818 to 2.222, while 3 fields showed a negative correlation between BSS frequency and local stellar
density with coefficients from -4.231 to -3.587. This supports the theory that binary mass transfer is the
primary mechanism for BSS formation in field stars.
| 37 |
Author(s):
Kina Fernandes.
Page No : 370-378
|
A Systematic Review of The Impacts of Predictive Maintenance Using AI in the Aerospace Industry
Abstract
Airlines and aerospace companies continuously seek to reduce operational costs while maintaining
the highest safety standards. Since aircraft maintenance constitutes a significant portion of these costs,
manufacturers and operators are exploring new methods to improve efficiency. One promising approach
is predictive maintenance, which leverages sensor data and machine learning algorithms to predict
component or system failures before they occur. Maintenance can then be scheduled at optimal times
to maximize component lifespan and minimize downtime. This paper employs a systematic literature
review method analyzing 60 sources to evaluate the impacts of predictive maintenance in the aerospace
industry. The findings indicate that predictive maintenance offers several benefits, including reduced
operational costs, enhanced safety, lower environmental impact, and improved passenger experience.
However, implementation challenges remain, particularly regarding data availability, lack of regulatory
standardization, limited technical expertise, and integration into legacy aircraft systems. This topic was
selected due to its significant potential as an emerging technology with transformative implications for
aviation. The primary limitation of this study is the limited number of available case studies; however,
all relevant cases identified were included in the analysis.
| 38 |
Author(s):
Abhinav Donepudi.
Page No : 379-392
|
Evaluating The Therapeutic Potential of MicroRNA and Artificial MicroRNA Overexpression in Huntington’s Disease
Abstract
Huntington’s disease is a neurological/genetic disorder that affects about 41,000 people in the United
States alone. Those with Huntington’s disease live their everyday lives with various symptoms that
may greatly hinder their ability to complete specific tasks. Without a cure for Huntington’s disease,
people with the disease continue living their lives with these symptoms, including behavioral and
movement changes. However, the overexpression of certain microRNAs and artificial microRNAs
may prevent neurodegeneration induced by Huntington’s disease. MicroRNAs, a naturally occurring
class of non-coding RNA, can regulate the expression of specific proteins by preventing the translation
of messenger RNA. Within neurological cells, the overexpression of microRNAs naturally found in
the nervous system can promote neurogenesis and neuronal function. This was found to occasionally
combat neurodegeneration found in various models of Huntington’s disease, suggesting its therapeutic
potential. Artificial microRNAs are genetically engineered microRNAs used to prevent the translation
of specific genes. They are different from microRNAs in that they can be engineered to target more
particular genes, such as the Huntingtin gene, unlike microRNAs, which usually only target genes that
are involved with general cell productivity. By targeting the mutant Huntingtin gene among models of
Huntington’s disease, it was consistently found that mutant Huntingtin mRNA and protein levels were
reduced, preventing neuronal death. Methods of delivery of these microRNAs and artificial microRNAs
include adeno-associated virus (AAV) vectors, lentiviral vectors, and exosomes, where AAV vectors,
specifically AAV5, were found to be the most effective as a delivery method.
| 39 |
Author(s):
Aisha Mukhamejanova.
Page No : 393-400
|
From Bench to Bedside: Evaluating CRISPR Strategies Reported Since 2020 for Correcting Alzheimer’s Disease Pathology
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of
dementia worldwide. It is characterized by the accumulation of amyloid plaques and neurofibrillary
tangles, often linked to pathogenic gene mutations. As no curative treatment is currently available,
researchers are increasingly exploring CRISPR-based strategies, such as base editing, prime editing,
and epigenetic editing, as potential therapeutic options. This review examines studies published since
2020 on the application of CRISPR technologies in the context of AD. The evidence highlights the
considerable potential of gene editing for targeting AD-associated genes and alleviating disease-related
pathology. However, all reported findings remain at the preclinical stage, as key barriers, particularly
low editing efficiency and delivery challenges, in delivering CRISPR components to the brain.
| 40 |
Author(s):
Grace Liu.
Page No : 401-412
|
Costco Wholesale: Performance, Strategies, and Outlook
Abstract
This study explores the strategic and financial development of Costco Wholesale Corporation,
emphasizing its membership-based revenue model, expansion strategy, and digital transformation.
Drawing on linear regression of nine years of historical data from Costco’s SEC filings (2016–2024),
this analysis projects the company’s financial performance through the period 2025 to 2034. The
models yield R-squared values of 99%, 97%, and 86% for membership revenue, total sales, and profit
margin, respectively, indicating strong growth stability from 2016 to 2024. Membership fees, which
accounted for approximately 66% of net income in fiscal year 2024, are identified as a key driver of
profitability. The model forecasts a 56% increase in membership revenue by 2034 compared to 2024,
with membership fees projected to comprise approximately half of total operating income. The study
also highlights emerging challenges, including market saturation in the United States and slower
international expansion, particularly in China due to localization barriers. Comparative analysis with
Walmart and Amazon reveals lagging adoption of e-commerce and artificial intelligence. The findings
suggest that while Costco’s core model remains robust, future competitiveness will depend on its ability
to adapt to digital innovation and evolving global market conditions.
| 41 |
Author(s):
Brandon Cha.
Page No : 413-424
|
What is the Relationship Between Fast Foods, Balanced Diets and Mental Health?
Abstract
The relationship between diet and mental health has become a central focus of modern scientific
research. This literature review examines how balanced diets and fast foods differently influence
mental health, emphasizing three main mechanisms: the gut microbiome, essential nutrient
availability, and cellular energy metabolism. A diverse gut microbiome supports regulation of the
gut-brain axis, while nutrient deficiencies are associated with increased susceptibility to depression
and anxiety. Given the rising prevalence of mental health disorders, nutritional psychiatry offers an
accessible and affordable avenue of potential treatment, however many people still underestimate or
neglect the importance of diet and nutrition. This review highlights the negative effects of fast food
and Western dietary patterns alongside the benefits of balanced nutrition, underscoring the necessity
for future longitudinal studies that minimize confounding variables, contain homogenous definitions
of terms such as anxiety and depression, and adopt identical methodologies for coherence and
applicability. However, this literature review confirms the correlation between diet and mental health,
as fast foods have negative cognitive effects and increased susceptibility to anxiety and depression,
while balanced diets can mitigate these effects.
| 42 |
Author(s):
Theodore Hwang.
Page No : 425-431
|
C1orf87, a predictive biomarker for lung adenocarcinoma
Abstract
Uncharacterized genes represent a substantial fraction of the human genome and remain largely
unexplored despite their potential importance in human physiology. C1orf87, also known as carcinoma
related EF-hand protein, has been annotated as a protein-coding gene, yet its molecular function and
disease relevance have not been established. The analysis of transcriptomic data from The Cancer
Genome Atlas (TCGA) comprising 576 lung cancer patients, encompassing more than 20,000 genes
per case, revealed its association with patient survival and distinct pathway alterations in gene set
enrichment analysis (GSEA). In addition, C1orf87 was found to be significantly upregulated in lung
adenocarcinoma relative to adjacent normal tissue. However, elevated C1orf87 expression correlated
with favorable overall survival (p = 0.097), suggesting a prognostic role. These findings support C1orf87
as a potential predictive biomarker in lung adenocarcinoma and highlight the value of investigating
previously uncharacterized genes in uncovering novel cancer biology.
| 43 |
Author(s):
Jayveer Kochhar .
Page No : 432-448
|
Multiple Input Neural Network with Fourier Series to Classify Variable Stars
Abstract
Measurement of astronomical distances, understanding of stellar evolution, and improvement of
our knowledge of galactic structure depend on accurate classification of variable stars. Conventional
classification techniques find it difficult to handle the rising volume of data produced by massive sky
surveys such as Large Synoptic Survey Telescope (LSST), All Sky Automated Survey for Supernovae
(ASAS-SN), and Optical Gravitational Lensing Experiment (OGLE). This work offers a hybrid deep
learning method using Multiple-Input Neural Networks (MINN) to improve classification accuracy by
means of image-based light curve analysis combined with astrophysical parameters derived via Fourier
decomposition and skewness analysis. Problems including class imbalance, phase misalignment, and
subtype distinction are addressed in the suggested approach. Minima Phase Standardization aligns
phase-folded light curves for uniformity; the Fourier Best Fit model extracts important coefficients
reflecting light curve shape. A Variable Star Light Curve Simulator creates synthetic data for
underrepresented classes, especially ACeps and Type II Cepheids, to reduce dataset imbalance,
therefore guaranteeing a more balanced training dataset. In ten epochs, the hybrid model attained
an overall classification accuracy of 89.8%; considerable gains for rare classes were obtained. For
common classes, the convolutional neural network (CNN) alone achieved 98.1% accuracy. This work
emphasizes, especially for rare and underrepresented variable star types, the need of integrating deep
learning with astrophysical insights to increase classification accuracy. By laying the groundwork for
automated, large-scale classification of variable stars, the proposed framework accelerates the study
of huge astronomical datasets and improves our knowledge of the structure and development of the
universe.
| 44 |
Author(s):
Sofia Geambazi.
Page No : 449-462
|
CRISPR-Cas13 Targeting of RNA in Glioblastoma Stem Cells: A Novel Approach to Disrupt Tumor Progression
Abstract
This review evaluates CRISPR-Cas13 as an RNA-targeting strategy to eradicate glioblastoma stem
cells (GSCs) by knocking down transcripts that sustain self-renewal (e.g., SOX2, c-MYC, EGFRvIII,
OLIG2). It focuses on guide design, delivery across the BBB to hypoxic/perivascular GSC niches, and
preclinical endpoints that predict reduced recurrence. Glioblastoma Multiforme (GBM) is the most
aggressive cancer of the brain, largely due to recurrence driven by Glioblastoma Stem Cells (GSCs).
Treatment failure arises from interrelated factors such as therapy resistance, blood-brain barrier (BBB)
impermeability, and immune evasion. CRISPR-Cas13 is a novel RNA-targeting system that directly
silences key oncogenic transcripts of GSCs such as SOX2, c-MYC, and HIF-1α. Cas13 enables allele
specific targeting, and a reduced risk of genomic integration compared to other genetic tools including
RNA-interference (RNAi), antisense oligonucleotides (ASOs), and Cas9. However, effective delivery
remains challenging as viral vectors, non-viral vectors, and exosomes each offer distinct trade-offs.
Combination strategies such as pairing Cas13 with immune checkpoint inhibitors (ICIs) may overcome
immune suppression and barriers posed by oncogenic drivers. In the near-term, lipid nanoparticles
(LNPs) with BBB-penetrating ligands are a promising approach for GBM treatment via GSC targeting,
however as the engineering of exosomes develops, their natural origins, high biocompatibility and
ease of integration into difficult tissues such as the brain becomes increasingly favorable. Ultimately,
advances in Cas13 engineering and delivery could enable durable GSC targeting and transform the
clinical outlook for GBM patients.
| 45 |
Author(s):
Rishi Desai.
Page No : 463-469
|
The Boeing 737 MAX Crisis: Public Trust, Aviation Safety, and Corporate Accountability?
Abstract
Imagine boarding a plane, unaware that a hidden software system—one you’ve never heard of—
could override the pilot and send the aircraft into a nosedive. When Boeing introduced MCAS, it
was meant to solve a problem. Instead, it created a deadly one. The truth is that this kind of event
happened in the series of accidents involving Boeing’s 737 MAX, which was found to have MCAS
(Maneuvering Characteristics Augmentation System) linked as the main cause of two fatal crashes
that killed 346 people. The crashes raised widespread concerns about engineering, regulatory, and
corporate responsibilities. Investigations revealed failures not only in technology but also in corporate
accountability and basic ethics. However, little research has examined how these failures affected
public trust in aviation safety, particularly on social media. Using BlueSky’s API, a dataset of 10,455
posts dating back to 2021 was collected and filtered from all of BlueSky’s posts using Boeing-related
hashtags to ensure relevance. VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment
analysis was applied to measure polarity scores across the dataset, using standard thresholds to classify
positive, neutral, and negative sentiment. Revealing that average sentiment remained negative, with a
mean composite score of –0.43 across the study period and sharp declines following major Boeing
related incidents. The Boeing 737 MAX crisis demonstrates how failures in corporate responsibility
and inaccuracies can weaken public trust in aviation safety. This trust can only be rebuilt through
better methods of transparency, enhanced regulation and enforcement, and a deepened commitment to
accountability to ensure this trust is maintained by the aviation industry.
| 46 |
Author(s):
Aryan Cherukuri.
Page No : 470-477
|
An Economic Review of Cheating in Competitive Swimming: Incentives, Benefits, Strategies, and Solutions
Abstract
Cheating in competitive swimming has long challenged the integrity of the sport, raising questions
about fairness, enforcement, and the motivations that drive athletes to bend or break the rules. This paper
explores the different ways cheating manifests in swimming, ranging from performance-enhancing
substances to more subtle violations of technical regulations. While these practices may provide
short-term advantages, they undermine both the credibility of competitions and the trust athletes and
spectators place in the sport. Beyond identifying the problem, this paper examines potential solutions,
including stricter enforcement mechanisms, education-based prevention strategies, and innovations in
testing and monitoring. By incorporating economic perspectives, the discussion highlights how rational
decision-making can be influenced by both structural rules and cultural norms within the swimming
community. Ultimately, the analysis suggests that effective solutions require a balance between
punishment and prevention, as well as recognition of the broader economic and psychological forces
at play. Through this lens, the paper not only addresses cheating in swimming but also points toward a
framework that can apply to other sports where fairness and integrity remain at risk.
| 47 |
Author(s):
Duc Anh Ngo.
Page No : 478-484
|
Influence of Community Engagement on Cryptocurrency Market Valuation
Abstract
Cryptocurrencies are volatile digital currencies based on a decentralized system. Their market
behavior, shaped primarily by communal factors such as developer activity and community engagement,
differs from that of traditional financial instruments, which are typically driven by intrinsic factors. This
study examines the impact of community engagement, as measured by developer activity on GitHub,
on the valuation and trading volume of decentralized assets. A quantitative research design is used
to analyze developer data from multiple cryptocurrencies. Statistical methods, including correlation
analysis, are applied to assess the strength of the relationships between developer activity, asset
valuation, and trading volume. Preliminary findings indicate a consistent correlation between developer
engagement and both asset valuation and trading volume, offering insight into what drives the success
of cryptocurrency projects. This research contributes to the rapidly growing field of cryptocurrency
market analytics, highlighting developer activity as a predictive indicator and a potential tool for
anticipating shifts in both market dynamics and community sentiment.
| 48 |
Author(s):
Alicia J. Lam.
Page No : 485-493
|
Enhancing Microorganism Classification Using Multinomial Logit Regression, k-Nearest Neighbor, and a Hybrid Approach
Abstract
The aim of this study is to address the current challenges in microorganism identification and
classification–particularly those that impede timely and accurate medical diagnoses and treatments
for patients. Recognizing the constraints of traditional bacterial classification processes, this study
explores the potential of machine learning in streamlining microorganism identification using their
morphological features. For this research, three modeling techniques were tested: multinomial logistic
regression (MLR), k-nearest neighbor (k-NN), and a novel hybrid model integrating the two. Using a
dataset sourced from Kaggle–a Google website that serves as a platform for members of the scientific
community to publish their datasets–and individually benchmarked in their abilities to accurately
distinguish between ten distinct microorganism species (Spirogyra, Volvox, Pithophora, Ulothrix,
Diatom, Fungi, Yeast, Rhizopus, Penicillium, Aspergillus sp., and Protozoa) based on twenty-four
numerical features detailing their geometry and structure. The experiment’s findings revealed that
while the k-NN model outperformed the multinomial logistic model, the hybrid approach yielded the
highest degree of accuracy in its classification of the ten microorganism species. In comparison to
conventional cultivation techniques employed in clinical microbiology, machine learning forgoes the
lengthy processing time associated with it, as it has the capability to accurately identify pathogens
based on existing data. As a result, patients–especially those in urgent cases–are able to receive rapid
diagnosis and necessary treatment before the bacterial culture is fully grown.
| 49 |
Author(s):
Annice Han, Bailee Kim, James Lim, Hannah Chae, Jasmine Liu, Woojoo Park, Elisha Kim, Miguel Shim, Jiyeon Hwang.
Page No : 494-500
|
Effects of Various Disinfectants for Algae Control in Hydroponic Nutrient Solution
Abstract
Climate change continues to threaten traditional agriculture. As a result, there has been an increased
interest in hydroponics as an alternative to the future food supply. However, algae growth in hydroponic
systems presents a significant challenge to plant health and productivity. For one, algae growth leads
to depleted oxygen and nutrient levels and root rot, notably decreasing the efficiency of vegetation
growth. This matter must be addressed by finding a means of controlling the algae blooms within a
hydroponic system to yield productive vegetation growth. We hypothesized that adding disinfectants to
hydroponic water could be an effective solution to solve this problem. To test our solution, we compared
algae growth in a control hydroponic nutrient solution with solutions containing 1% of either 99.5%
ethanol, 99% isopropyl alcohol (IPA), 3% bleach, and 3% hydrogen peroxide (H2
O2
). Solutions were
prepared in 500 mL beakers and exposed to sunlight, with photos taken every 12 hours over 4 days to
observe and document the algae growth. Out of four different common disinfectants, H2
O2
was the most
effective in inhibiting algae growth. Our findings will enable a cheaper method to control algae growth
in hydroponics using commonly available disinfectant which is 3% H2
O2
.
| 50 |
Author(s):
Saisha Swain.
Page No : 501-508
|
The Impact of Ticket Prices on Attendance at the United States Grand Prix in Austin (2018–2023)
Abstract
While Formula 1 has experienced rapid growth in popularity in the United States, little research has
examined how ticket prices affect attendance—representing a key gap in sports economics. This study
examines the relationship between ticket prices and attendance at the Austin Grand Prix from 2018 to
2023, using publicly available data from official Formula 1 sources, Motorsport.com, GP Today, and F1
Destinations. The analysis focuses on General Admission and Grandstand passes to assess how changes
in pricing align with shifts in weekend attendance. Results indicate that, despite significant increases
in ticket prices, attendance rose consistently across the period. While factors such as expanded media
exposure, the addition of new U.S. races, and the influence of Drive to Survive likely contributed to this
growth, rising costs did not appear to discourage fans. These findings suggest that Formula 1’s U.S.
audience demonstrates relatively low-price sensitivity, highlighting the sport’s success in cultivating a
loyal and economically resilient fan base.
| 51 |
Author(s):
Allen Ping-An Chen, Steven Lu.
Page No : 509-519
|
Physical Insights into Homemade Radish Pickling: Optimizing Flavor and Health through Capillary Action and Osmosis
Abstract
Based on the motivation of making home-cooked meals more tasty while not harming the body
by consuming too much seasoning. This study explores the best way to pickle radish using three
different types of seasoning liquids: chicken broth, soy sauce, and salt water. The goal of this study is
to find out how different factors, including geometrical properties of the radish sample used and the
solution’s fluid properties, affect the pickling process. The mechanism behind the pickling process is
f
irst revealed by including capillary action and osmosis into theoretical and experimental discussions,
showing the dominance of osmosis. The distribution of gray scale value across the diameter is also
measured, showing relatively uniform pickling for soy sauce and salt water. Through the experiment,
higher solution concentrations accelerated water loss in soy sauce and salt water through osmosis, as
shown by the fact that radish pickled in 80% soy sauce decreased in weight at a rate of 0.078 g/min,
while those pickled in 60% soy sauce decreased at a rate of 0.052 g/min. On the other hand, chicken
broth showed irregular weight changes due to its complex solutions and molecules, which is discussed
and explained by the Stokes-Einstein relation. For the contact area ratio, 59.8% is found to be the most
ideal ratio as it maximizes the weight reduction rate, achieving a maximum value of 0.361 in both soy
sauce and salt water. Lower water content created by predrying the sample leads to better pickling
results for soy sauce and salt water, especially around 86%, but opposite for chicken broth. This study
provides a comprehensive view of a qualitative explanation for physical insight into radish pickling by
various physics models referenced, providing a way to healthier vegetable pre-processing.
| 52 |
Author(s):
Ayaan Anand, Ayush V Madhar, Gitanjali Srivastava, MD.
Page No : 520-526
|
Artificial-Intelligence (AI) Enhanced Sonar Technologies and Emerging LiDAR Applications in Body Composition and Diagnostics
Abstract
This paper explores the integration of Sonar and LiDAR technologies in body composition imaging
and biomedical applications. Sonar, primarily used in ultrasound and elastography, offers non-invasive,
radiation-free imaging and therapeutic capabilities, including emerging uses in cell manipulation and
tissue engineering. LiDAR, traditionally used in environmental mapping, is now being adapted for
medical imaging through surface mapping, morphological analysis, and radiation-free 3D scanning. The
synergy between these sensory technologies and artificial intelligence, particularly convolutional neural
networks, enhances diagnostic precision, patient monitoring, and procedural guidance. This review
highlights the strengths, limitations, and emerging innovations of Sonar and LiDAR in advancing non
invasive diagnostics and personalized medicine.
| 53 |
Author(s):
Yuhe Wang.
Page No : 527-534
|
The Relationship Between Anxiety, Confidence, and Athletic Performance: A Literature Review
Abstract
Athletes strive to perform at their best, especially under pressure. Psychological factors such
as anxiety and confidence are believed to influence outcomes and have long been studied in sport
psychology. A narrative literature review was conducted using Google Scholar to identify peer-reviewed,
open-access, English-language studies published between 1988 and 2022. A total of 12 studies met
inclusion criteria and were included in this review. The sample sizes of each study varied, from fewer
than 15 participants to more than 400. A total sample size of 1403 participants were included in this
review. Across studies, higher anxiety was generally negatively correlated with performance, although
effects varied due to factors including but not limited to anxiety type, athletes’ interpretation of
symptoms, and mediating variables. Findings indicate that higher confidence was positively correlated
with performance outcomes. Managing anxiety and building confidence both appear important for
optimizing sport performance. Future work should test practical interventions (e.g., mindfulness and
coping-skills training) that target these factors.
| 54 |
Author(s):
Seungbeom Cho.
Page No : 535-544
|
New Sustainable Atmospheric Water Harvester for Multi-Daily Freshwater Supply
Abstract
With increasing water scarcity, atmospheric water harvesting (AWH) provides a sustainable solution
by extracting liquid water directly from water vapor. Among emerging AWH sorbents, metal–organic
framework (MOF) is particularly attractive due to its high porosity and strong water uptake. However,
MOF-based devices are typically either passive limited to once-daily cycles or active systems that
require external electricity. This study presents a sustainable atmospheric water harvesting device that
collects water multiple times per day without external electricity using hand-operated vacuum pump
and a low-temperature sink. To improve portability, MOF powders were immobilized as beads within
a porous sponge, achieving water uptake comparable to powders but with faster uptake rates. Vacuum
assistance reduced the cycle time by up to 75.5% and increased the water yield per cycle by up to 31.4%
under controlled test conditions. In prototype operation, 10 g of MIL-101(Cr) produced about 7 g of
water within 1 hour using a hand-operated vacuum pump, compared to about 5 g over 3.5 hours without
vacuum assistance. These results validate a multi-cycle, electricity-free harvesting strategy for water
scarce environments.
| 55 |
Author(s):
Aditi Senthil Kumar.
Page No : 545-553
|
How Does the India Pakistan Conflict Highlight the Limitations of Peacekeeping Structures and the Enforcement of War Laws?
Abstract
Stemming from the broader India-Pakistan conflict, the Kashmir dispute is often described as the
oldest unresolved international conflict in the world today. This protracted territorial dispute profoundly
highlights the limitations of international peacekeeping structures and the enforcement of war laws,
particularly concerning the Fourth Geneva Convention. While the conflict traces back to the 1947
Partition, its persistence has entrenched militarization, human rights abuses, and recurring interstate
clashes, most recently exemplified by India’s 2025 Operation Sindoor. International frameworks such
as the Fourth Geneva Convention guarantee protections for civilians, yet in Kashmir these rights are
routinely violated with little accountability. Adopting a case study analysis, this research assesses
the intersection of peacekeeping frameworks, sovereignty claims, and human rights discourses using
academic scholarship, UN documents, legal texts, and human rights reports. Focusing on three themes,
the enforcement gap in the laws of war, the role of sovereignty and bilateralism in restricting oversight,
and the structural limits of peacekeeping mandates, findings show that India’s rejection of third-party
involvement, Pakistan’s internationalization strategy, and the persistence of laws such as the Armed
Forces (Special Powers) Act (AFSPA) and the Public Safety Act (PSA) perpetuate impunity. The
Kashmir case demonstrates how, in sovereignty-driven conflicts, peacekeeping collapses into symbolism,
underscoring the urgent need to reconceptualize it as a legal framework accountable to international
humanitarian law.
| 56 |
Author(s):
Emily Shao.
Page No : 554-565
|
Held Together by Mothers: Revaluing the Unpaid Emotional and Physical Labor of Care Work in A Neoliberal Economy, the Economy Needs What It Hides
Abstract
This perspective argues that the labor of mothering, both the physical demands of reproductive work
and the invisible burden of emotional care, forms the unacknowledged foundation of economic and
social life. Under neoliberalism, this labor is not simply forgotten but actively exploited, its value erased
through narratives that frame care as love rather than work. The COVID-19 pandemic briefly exposed
society’s reliance on unpaid and underpaid caregiving, yet no structural solutions emerged. Movements
like the Global Women’s Strike and their Care Income Now campaign challenge this paradigm,
demanding not only policy reforms but a radical redefinition of work itself; one that frames caregiving as
a collective responsibility rather than a private expectation. Compensating mothering is not merely about
economic justice but correcting a systemic distortion in how value is assigned. Evidence from policies
like the expanded U.S. Child Tax Credit and Finland’s home care allowance demonstrates that investing
in caregivers reduces poverty, boosts labor participation, and improves child well-being. Drawing on the
insights of Silvia Federici and the Wages for Housework movement, this argument contends that unpaid
reproductive labor sustains all other forms of work. Compensation is not the commodification of love but
a refusal of its exploitative nature. Recognizing mothering as labor is both an act of economic justice and
a demand for structural transformation.
| 57 |
Author(s):
Mihir Vinnakota.
Page No : 566-573
|
Disparities in Compensation, Infrastructure, and Workforce Stability: A Comparative Study of Nonprofits and Corporations
Abstract
Nonprofits are instrumental in today’s efforts to support under-resourced communities. However,
many nonprofits have underlying structural inefficiencies that hinder their ability to serve society. This
study analyzes the organizational differences between the corporations and nonprofits by comparing
categories like employee turnover, workforce size, wages, infrastructure spending, and reliance on
government funding. The research aims to identify these differences, understand how those differences
negatively impact nonprofits, then discuss how nonprofits can improve organizational efficiency. Earlier
research has shown that nonprofits face financial struggles. However, few studies have compared these
limits directly to corporate data. Using data from the Bureau of Labor Statistics, IRS Form 990 filings,
SEC 10-K reports, and nonprofit workforce surveys, the study showcases a clear disparity. Nonprofits
pay lower wages, have fewer chances for advancement, deal with higher turnover, and spend less on
infrastructure. The study discusses the further implications of this data, and discusses how adopting
some corporate practices, along with policy support, could help nonprofits achieve their missions more
effectively over time.
| 58 |
Author(s):
Mohnish Sivakumar.
Page No : 574-580
|
Improving Sentiment Analysis of Tamil-English Code-Mixed Sentences
Abstract
This paper investigates sentiment analysis for Tamil-English code-mixed text, a common feature
of social media communication in multilingual regions. Code-mixing in Romanized Tamil introduces
challenges such as inconsistent spelling, transliteration, and noisy syntax that traditional models are not
designed to handle. Using the FIRE-DravidianCodeMix 2020 (hereafter FIRE2020) dataset, lexicon
based methods, classical machine learning models, deep learning (LSTM), the multilingual transformer
RemBERT, and hybrid approaches combining lexicon-based features with machine learning models were
evaluated on sentiment classification. Results showed that classical models such as Logistic Regression,
Naive Bayes, and SVM achieved the most stable performance, reaching around 69% accuracy with
weighted F1-scores near 0.60. Deep learning and transformer models offered no clear advantage, with
both LSTM and RemBERT performing slightly lower than the classical models, plateauing near 67%
accuracy and weighted F1-scores around 0.54. These results emphasize that lightweight statistical
models remain the most reliable in noisy and resource-constrained code-mixed environments, while
deep learning and transformer architectures require greater adaptation to succeed.
| 59 |
Author(s):
Deanna Schwarzberg.
Page No : 581-587
|
How Social Media Use Alters Mental Health and Cognitive Behavior
Abstract
Social media has taken over many people’s lives. More than half of the world’s population uses
it, with a large percentage being teenagers. Although social media can have benefits, it has many
negative implications, and people take several risks when using the platforms. Several studies test the
way social media has affected attention-deficit/hyperactivity disorder (ADHD) symptoms, attention
issues, depression, anxiety, and insomnia. This review provides evidence that social media could harm
a person’s mental health. The studies summarized suggest that social media may be the cause of an
increase in ADHD symptoms, attention issues, anxiety, and depression symptoms, as well as sleep
disturbance. Altogether, these studies indicate that social media negatively impacts a person’s mental
health. However, more research should be done to better understand the intricacies of the relationship
between social media and mental health, which further the knowledge about the relationship. Because
of the widespread use of social media, understanding how it can negatively impact a person’s mental
health has become important. This article is to review current research examining how social media use
influences mental health outcomes, particularly ADHD, anxiety, depression, and sleep disturbance, to
identify key behavioral and cognitive pathways affected by excessive use.
| 60 |
Author(s):
Sankalp Tank.
Page No : 588-593
|
How Can Disparate Retrieval Methods Work Together Effectively?
Abstract
Information retrieval (IR) methods, systems that find relevant information from large datasets, are
separated into sparse and dense methods. This study investigates how these two types of methods can
work in tandem to optimize speed, indexing cost, and accuracy when answering structured queries on
large datasets. This research implemented and benchmarked multiple retrieval methods, ranging from
sparse methods TF-IDF, BM25, and an enhanced version coined SUPER_BM25, to dense methods
SPLADE and COLBERT. These methods were queried to measure the recall, indexing time, and
query time of each. The results indicated that dense methods achieved fast retrieval times at the cost of
precision; conversely sparse methods were incredibly accurate, but they took significantly more time.
Based on these results, a funnel system of these disparate methods was created, where each method
worked in tandem to optimize speed and accuracy. This funnel system reduced indexing time by 23.7%
and query time by 99% when compared with COLBERT while retaining comparable recall scores. The
funnel system achieved these high indexing and query speeds by having TFIDF, BM25, and SUPER_
BM25 cull 90.4% of the dataset, then giving SPLADE and COLBERT the remaining data to accurately
rank it. This hybrid funnel approach presents a scalable and cost-efficient framework for real-word
information retrieval, enabling faster, more accurate search across large datasets.
| 61 |
Author(s):
Tyler Oyang.
Page No : 594-600
|
The Potential Role of GLP-1 Receptor Agonists for the Treatment of Alzheimer’s Disease
Abstract
Alzheimer’s Disease (AD) is currently one of the leading causes of mortality worldwide and is
characterized by excessive accumulation of amyloid-β plaques, tau protein hyperphosphorylation, and
chronic neuroinflammation. However, there are no effective cures or therapies for the treatment of AD.
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), a class of medication commonly used to treat
type 2 diabetes and obesity, have recently emerged as novel candidates for AD therapy. Preclinical
studies demonstrate that GLP-1 RAs efficiently cross the blood-brain barrier, reduce amyloid burden,
attenuate neuroinflammation, and improve neuronal survival. Furthermore, early clinical studies show
their potential for cognitive benefits in patients with AD. This review paper examines the molecular
mechanism of GLP-1R signaling in the central nervous system and its therapeutic effects in the treatment
of AD. Additionally, emerging therapeutic strategies, such as dual agonist treatments and organoids, offer
potential increases in research and development speed, paving the way for promising future development
of GLP-1 RAs and their translational progress into AD. Together, these findings suggest that GLP-1RAs
are promising therapies for the treatment of AD in humans.
| 62 |
Author(s):
DA-YOUNG CHOI.
Page No : 601-606
|
Language, Consciousness, and the Human Interior: Shakespeare’s Hamlet and Woolf’s to the Lighthouse
Abstract
This narrative review examines the ways that Shakespeare’s Hamlet and Virginia Woolf’s To the
Lighthouse expose human consciousness in different linguistic and stylistic modes: soliloquy and stream
of-consciousness. Relying on A. C. Bradley’s interpretation of the soliloquy, William James’s conception
of consciousness as a “stream,” and the aesthetics of modernism, this paper proposes that both the
authors dramatize the limits of language in representing the mind. While Shakespeare’s soliloquies turn
introspection into a form of theatrical performance, Woolf’s prose places the reader within a continuous
f
low of perception and memory. Positioned respectively within Renaissance humanism and post-war
modernism, these methods are used to show that literature plays the important dual role of mirror and
model. This review focuses primarily on how Hamlet and To the Lighthouse render consciousness visible
through language shaped by historical and cultural context.
| 63 |
Author(s):
Ikya Muppuri.
Page No : 607-614
|
Therapeutic Approaches for Chronic Spontaneous Urticaria: Current and Emerging Options
Abstract
Chronic spontaneous urticaria (CSU) is a recurrent, mast cell-mediated skin condition characterized
by wheals, angioedema, or both lasting over six weeks without a known cause. Its underlying mechanisms
are still being researched but involve some immune dysregulation, including Immunoglobulin E (IgE)
mediated mast cell activation and autoimmune signaling. This leads to unpredictable flare-ups that
significantly affect a patient’s quality of life. Current treatment guidelines recommend a stepwise
approach. Second-generation H1-antihistamines are the first-line therapy, though many patients
experience incomplete relief even after increasing the dosage. Omalizumab, an anti-IgE monoclonal
antibody, is the standard second-line treatment and has transformed management for patients who
do not respond to antihistamines by providing significant symptom control and improving quality of
life. Dupilumab, which targets the Interleukin-4 (IL-4) and Interleukin-13 (IL-13) pathways, and
Tezepelumab, which blocks thymic stromal lymphopoietin (TSLP), have shown substantial efficacy in
recent trials, expanding biologics available for more severe cases of CSU. Emerging therapies continue to
advance the field by targeting different points in mast cell activation. Remibrutinib, a Bruton’s tyrosine
kinase (BTK) inhibitor, and Barzolvolimab, an anti-KIT proto-oncogene, receptor tyrosine kinase (KIT)
monoclonal antibody, has demonstrated promising results in reducing symptoms and improving disease
control. Together, these treatments mark a shift towards precision medicine in CSU, focusing on targeted,
mechanism-based therapies that not only improve symptom relief but also aim for long-term remission
and reduced disease burden. This review focuses on approved and emerging biologic therapies for CSU
from the past to present, with significant developments from 2014 to 2025.
| 64 |
Author(s):
Michelle Li.
Page No : 615-623
|
Timing the Money: Temporal Dynamics of 2016 U.S. Presidential Campaign Disbursements from Transaction Totals
Abstract
This study investigates the temporal dynamics of campaign disbursements during the 2016 United
States presidential election using daily expenditure data from the Federal Election Commission (FEC),
compiled through Kaggle’s public campaign finance dataset. Focusing on six major federal candidates,
the research employs linear trend analysis, quadratic trend analysis, and seasonality (residual) analysis to
examine how disbursement patterns evolve across the election cycle. Results reveal that overall spending
trends are weakly linear, with low explanatory power (R² ≤ 0.08), while quadratic models modestly
improve fit (R² ≤ 0.12), uncovering nonlinear behaviors such as mid-period surges and late-cycle
accelerations. Seasonality analysis highlights substantial differences in volatility, with one candidate
exhibiting pronounced episodic bursts and others maintaining steady expenditure pacing. Collectively,
the findings suggest that campaign spending is event-driven rather than time-driven, responding to
strategic milestones rather than following smooth temporal growth. Policy recommendations emphasize
the need for real-time expenditure monitoring, proportional pacing regulations, and data-driven budgeting
frameworks to enhance transparency and efficiency. Future research should incorporate stochastic and
autoregressive models (e.g., random walk) and link financial trajectories to key political events to better
capture the dynamic and strategic nature of modern campaign finance behavior.
| 65 |
Author(s):
Ava Gordon.
Page No : 624-635
|
Clicks to Cash: Understanding Loyalty’s Impact on Platform Economics
Abstract
Quick commerce platforms have made rapid delivery of food and groceries routine, yet their route to
sustainable profitability remains uncertain. This study examines whether customer loyalty contributes to
f
inancial stability and unit economics in the quick commerce sector. We employ a mixed-methods design
that combines a literature review, a survey of 100 customers conducted in August 2025, and regression
analysis to examine the relationship between loyalty drivers and customer satisfaction. Results show that
loyalty, convenience, and promotions are positively associated with satisfaction, emotional attachment
is the strongest predictor, and repeated usage is negatively associated with satisfaction, suggesting
friction for heavy users. Triangulated with industry evidence, the findings indicate that loyal customers
can increase order density and reduce reliance on costly acquisition discounts, thereby improving cost
efficiency. Companies should shift resources from broad discounting toward retention levers such as
reliable service, faster delivery, and personalized engagement. The study contributes to the sustainability
debate by connecting consumer behavior to operational and financial outcomes in quick commerce.
| 66 |
Author(s):
Yufei Xiao.
Page No : 636-643
|
Comparing Individual and Joint Logistic Models for Autism Screening: A Study of Family History Associations
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by
differences in communication, behavior, and cognition, with significant social and familial implications.
Understanding how familial background contributes to autism-related behavioral traits remains an
essential research goal. This study utilizes a publicly available dataset from Kaggle, comprising more
than 700 respondents, to examine the relationship between family history of autism and ten standardized
screening items (A1–A10). Two complementary statistical frameworks were employed: individual binary
logistic regression models, which estimate the item-level association between family history and each
screening response, and a joint Generalized Estimating Equation (GEE) model, which accounts for
within-subject correlation among multiple items. Results from the logistic regressions reveal significant
positive associations for six items (A1, A3, A4, A5, A6, A9, and A10), with odds ratios for these
significant items ranging from approximately 1.7 to 3.5, clarifying that non-significant items (e.g., A2 =
1.56, A8 = 1.25) are not included in this range. The joint GEE analysis further confirms an overall odds
ratio of 1.86 (p < 0.001), indicating that participants with a family history of autism are nearly twice
as likely to respond positively to ASD-consistent screening indicators. Together, these findings provide
statistical evidence for a familial component in autism-related behavioral expression and demonstrate the
value of integrating individual and joint modeling techniques in autism data analysis.
| 67 |
Author(s):
Hsuan-Te Lee, Luen-Dau Li, Nan-Kuei Chen.
Page No : 644-655
|
From Healthcare Accessibility to National Output: How Technology-Driven MRI Improvements Correlate with the Gross Domestic Product
Abstract
Magnetic resonance imaging (MRI) is among the most transformative medical technologies, yet its
widespread adoption in routine clinical practice remains constrained by high economic and technical
barriers. According to conservative estimates from our research, expanded MRI accessibility is
estimated to increase GDP by a range of 0.0095% to 0.0117% in relative terms, reflecting measurable
macroeconomic benefits. Recent advances in MRI, such as AI-enabled image reconstruction for
accelerated scans, cost-effective manufacturing, and the emergence of portable low-field scanners,
are poised to improve accessibility and enhance diagnostic capacity. These advances are expected
to correlate with national economic indicators such as the gross domestic product (GDP). This paper
reviews recent innovations in MRI and focuses specifically on one economic pathway: how mortality
reductions associated with improved MRI accessibility correlate with national economic output through
changes in the effective labor supply. Using a health-augmented Cobb–Douglas production framework,
a model that incorporates health capital as a factor influencing productivity and output and integrating
data from existing clinical MRI reports into a simplified model, we conservatively estimate that
such mortality improvements could generate short-run national output growth consistent with these
projections. Although these short-run gains appear statistically modest, the long-term implications are far
more significant. Clinical innovations not only improve healthcare outcomes but also create substantial
social and economic returns when considered over extended time horizons. Our findings underscore
the importance of sustained investment and research in advanced diagnostic technologies such as MRI,
highlighting their dual role in promoting public health and driving economic growth.
| 68 |
Author(s):
Luca Svendsen.
Page No : 656-662
|
The Impact of Undocumented Workers on U.S. Agriculture
Abstract
Undocumented immigrants comprise a significant portion of the U.S. agricultural labor (42.03%).
The Trump administration has emphasized its position on heightened enforcement and removals. These
policy changes could reshape labor availability and costs across farm supply chains. A supplementary
qualitative analysis traces supply-chain effects from farm to distribution. Supplementary findings
indicate that undocumented agricultural labor associated with a greater state-level output and large
scale reductions in this workforce may correspond to lower domestic production and increased supply
chain costs. Analysis of these results underscores the economic relevance of undocumented labor. This
study employs a 2023/2025 state-level linear regression model for variables affecting agricultural output:
population, per-capita income, total unemployment, and census-region indicators (N = 50 states; adjusted
R² = 0.773; F = 13.28 (Equation 4). This manuscript’s analytical data are sourced from the most recent
2023 and 2025 agricultural labor datasets to assess recent indicators of successful agriculture. While
regression data is relatively current, it remains a retrospective analysis of indicators. The regression
should be considered alongside 2025 immigration policy to contextualize potential future implications.
Results show that population is a positive correlation of agricultural output (β1
= 1.3×10-07; p < 0.001),
while unemployment shows no significant effect, and several eastern regions display lower output relative
to others.
| 69 |
Author(s):
John Doan.
Page No : 663-669
|
Computational Investigation of Aerodynamic Forces on an Airfoil with Variations of the Reynolds Number and its Components
Abstract
The lift force, drag force, and lift-to-drag ratio are essential performance metrics for any airfoil. Several studies have investigated these aerodynamic forces over a range of Reynolds numbers, but critically, detailed investigation of how the aerodynamic forces vary with response to independent changes in velocity, density and viscosity are scarce. Additionally, how the lift-to-drag ratio varies with the Reynolds number, Re, and individual variables within the Reynolds number is also unclear. In this study, computational fluid dynamics is used to assess the aerodynamic forces acting on an arbitrary airfoil over 3.3 × 10^2 < Re < 6.8 × 10^6 by independently varying fluid velocity, density and viscosity. The lift-to-drag ratio was found to be approximately constant for 9.8 × 10^5 < Re < 6.8 × 10^6. At Re = 3.3 × 10^4, the lift-to-drag ratio was found to depend on the specific variables used to alter Re, likely due to shear force becoming more significant at low Re. Results from this work provide compelling evidence that the lift, drag, and lift-to-drag ratio vary not only as a result of variation of Re but also due to independent variation of components of Re. Future studies analyzing different metrics with respect to Re should specify what variables are varied to avoid ambiguity.
| 70 |
Author(s):
Max Hong.
Page No : 670-675
|
Unpredictable Protectionism: The Global and Corporate Consequences of Trump’s Tariffs
Abstract
This paper shows the global and corporate results and consequences of President Donald Trump’s tariff
policies, with attention to the U.S.-trade relationship. In the beginning of 2018, the Trump Administration
launched a series of tariff circumventions justified by concerns over intellectual property theft and unfair
trade practices with other countries, especially China. These measures rapidly increased average tariff
rates on Chinese goods, peaking at an unprecedented level of 145% by 2025. Using Government data,
economic forecasts, and a direct case study from a Chinese pharmaceutical biotech manufacturer, this
research highlights how tariff volatility disrupted global supply chains, destabilized markets, and forced
companies to adapt and develop new ways of offshore production. Moreover, this study situates Trump’s
policy within the context of U.S. protectionism, from past events, the Smoot-Hawley Tariff act of 1930 to
current trade wars and considers their broad economic impact. Ultimately, the analysis demonstrates that
unpredictable protectionism undermines the stability on which global trade depends.
| 71 |
Author(s):
Sophie Chan.
Page No : 676-684
|
Meritocracy and Other Obstructions to Gradeless Learning
Abstract
While recent attention has often been directed at criticizing the current prevalent forms of grading,
much less attention has been directed toward the proposed alternative: gradeless learning. The champions
of this new approach argue for its ability to promote student wellbeing and good learning habits, all
while ensuring the academic rigor of the material students are taught. However, such promises have
not always been fulfilled, and gradeless learning remains a method of education largely uncommon in
schools. This study aims to investigate the difficulties of utilizing the new approach, or barriers to its
success. This narrative review consists of 13 empirical studies and 6 conceptual sources from 1973
2025, spanning 8 countries and 12 schools. The results found suggest the existence of barriers not only
in the implementation of such a method of learning, but also within societal constructs like the proposed
meritocracy. Students perceived barriers in the implementation processes as negative effects of variation,
citing too much variation in communication, teacher understanding and commitment, and scoring as
issues that compromised their view of the approach. It was also found that outside pressures had an
impact on the effectiveness of gradeless learning achieving its goals. Social constructs like meritocracy
instill in students and parents alike the idea that hard work means good grades, and in turn, a good
future. The addition of gradeless learning interrupts such a construct as it introduces a novel method of
communicating merit, leading students to doubt its ability to properly communicate to institutions like
colleges.
| 72 |
Author(s):
Vito Hsu.
Page No : 685-692
|
Pricing, Perception, and Popularity: Modeling the Impact of Discounts and Categories on Product Ratings and Engagement in E-Commerce Platforms
Abstract
Online shopping has changed how people buy products by offering more access, choices, and
convenience. Still, success in e-commerce depends heavily on pricing, discount strategies, and customer
response. This project examines how product price and discount level influence customer satisfaction
(ratings) and customer engagement (review counts) using a public Kaggle dataset from 2022 that
includes pricing, discount fractions, ratings, review counts, and product categories. Three models were
used: a multiple linear regression for ratings, a Negative Binomial regression for review counts, and
a joint model that estimates both together. The results show that a one–standard deviation increase
in log price raises average product ratings by 0.047 points, but lowers rating counts by 0.171 on the
log scale—meaning higher-priced items are rated better but reviewed less. Larger discounts show the
opposite pattern; a one–standard deviation increase in discount fraction decreases ratings by 0.053 and
reduces review counts by 0.152, suggesting that heavy markdowns may hurt both perceived quality
and engagement. Category differences also appeared: Electronics products received significantly more
reviews (0.672) but slightly lower ratings (–0.127), while Home & Kitchen and Office Products performed
worse across both outcomes. The joint model provided the most stable estimates and helped connect
the relationships between satisfaction and engagement. Overall, the study demonstrates that pricing and
discount strategies strongly shape how customers rate and interact with products online.
| 73 |
Author(s):
Shreya Rajeev, Aditya Rajeev.
Page No : 693-703
|
Statistical Evaluation of Microbial Additives and Fertilizers on Bush Bean Growth in Simulated Lunar Soil
Abstract
A significant challenge in establishing sustainable human settlements on the Moon lies in cultivating
food within extraterrestrial environments. Lunar regolith, the Moon’s surface material, is composed of
rock fragments, minerals, and volcanic glass, and poses substantial limitations for plant growth due to its
lack of essential nutrients. This study employs statistical regression analysis to evaluate the correlation
and impact of microbial additives and nitrogen, phosphorus, potassium (N-P-K) fertilizers on the growth
of bush bean (Phaseolus vulgaris) seeds in a lunar soil simulant. The microbial additive used, TPS Plant
Foods Billions of Microbes, contains five strains of Bacillus bacteria, four strains of mycorrhizae, and
one strain of Trichoderma. The fertilizer applied was Fruit & Bloom Booster NPK 2-15-15. Bush beans
were chosen for their rapid germination, resilience to temperature fluctuations, and disease resistance.
Specific regression analyses were conducted to assess the individual and combined effects of nitrogen,
phosphorus, potassium, and microbial inputs on plant growth. The results demonstrated that both TPS
Billions and nitrogen significantly enhanced plant growth, as evidenced by a 500–2000% increase
in number of leaves and leaf area in the 60% lunar soil mixture. These findings suggest that targeted
microbial and nitrogen-based amendments can effectively support plant cultivation in lunar regolith,
offering valuable insights for future lunar agricultural systems and long-term human habitation.
| 74 |
Author(s):
Victoria Lynn Barbara Mvondo.
Page No : 704-712
|
Why is Africa Still Poor? The Historical and Social Factors Behind Africa’s Struggle with Economic Stability
Abstract
Africa’s economic performance compared to the rest of the globe is poor. The continent struggles
with economic instability, facing challenges like low gross domestic products (GDP) and limited
industrial growth. Additionally, the civil wars and conflicts that displace millions in Africa exacerbate
poverty and war-torn regions find it nearly impossible to create a stable environment for economic
development. Many African countries struggle to offer basic education resources, limiting the youth’s
access to skills required to secure jobs offering high wages. As a result, future generations are stripped
of opportunities for advancement, continuing the cycle of generational poverty. In addition, corruption
and unstable governments prohibit resources from contributing to development projects. Lastly, Africa
is the continent with the highest poverty rates, lowest adult literacy rates, lowest Corruption Perception
Index score, and is the most conflict-prone continent. This paper argues that Africa’s struggles with
economic development stem from the destruction of institutions caused by the transatlantic slave trade,
which fostered a survivalist mindset and corruption still present today. However, by understanding
these root causes, African countries can break the cycle through institutional reform and a cultural shift
towards collective progress.
| 75 |
Author(s):
Irmak Bilici.
Page No : 713-720
|
Patriarchal Norms in Turkey and Their Influence on Women’s Development and Domestic Roles: A Literature Review
Abstract
Patriarchy refers to a social system in which positions of power and authority are primarily held by
men. In Turkey, patriarchal structures continue to shape gender expectations, often reinforcing women’s
association with domestic responsibilities. These norms are frequently linked to women’s biological
role as birth‑givers, which has historically tied them to caregiving and household duties, although their
roots are not yet fully explained. This systematic review aims to understand how patriarchal structures
and gender roles in Turkish society affect women’s psychological and behavioral development and their
participation in the labor force. Open access articles published in peer-reviewed journals between 2008
and 2025 were searched on Google Scholar. A total of 10 eligible studies (6 in English, 4 in Turkish)
were included in the current review. Findings revealed five thematic categories affecting women’s
development and potentially workforce participation: (1) Traditional Gendered Domestic Roles in a
Patriarchal Society, (2) The Religious Influence of Islam, (3) Social and Structural Barriers to Workplace
Participation, (4) The Role of Education and (5) Psychological and Emotional Impacts of Patriarchal
Conditioning. Overall, the literature indicates that entrenched gender norms, institutional gaps, and weak
policies sustain Turkey’s patriarchal structures. Key barriers to women’s workforce participation include
inadequate employment laws, childcare demands for young children, and expectations of domestic roles.
Education consistently emerges as the strongest driver of women’s labor force participation.
| 76 |
Author(s):
Srithan Devarashetty.
Page No : 721-730
|
The Influence of Estrogen and Estrogen Receptors on the Sex-Based Differences of the Immune Response to Melanoma
Abstract
Melanoma is a highly mutagenic cancer that disproportionately affects men in comparison to women.
Women have been found to be diagnosed less often than men, recover significantly better, and have a
lower mortality rate that is almost half that of men for cutaneous melanoma, which is melanoma of the
skin. This paper reviews the underlying biological factors for these sex differences. It was found that
these differences can be attributed to the influence of estrogen signaling on melanoma tumor cells as well
as innate and adaptive immune compartments. Estrogen suppresses tumor proliferation and enhances the
development and activity of the immune system. This could be part of why women have an advantage
since estrogen is their primary sex hormone. Together, these findings display that estrogen pathways may
be an effective target to treat melanoma patients. Further research is required to test this hypothesis and
these findings highlight the importance of incorporating sex-based biology into future melanoma studies.
| 77 |
Author(s):
Meghana Nandigam.
Page No : 731-739
|
Digital vs. Physical Nudges: How Amazon and Walmart Shape Consumer Decisions
Abstract
This paper looks at how Amazon and Walmart use psychology to influence the way people shop. Using
ideas from behavioral economics, it focuses on four main strategies: scarcity cues, loyalty programs,
price framing, and anchoring. Amazon does this mostly through digital tools like Prime, countdown
timers, and personalized pricing, while Walmart relies on in-store tactics such as product placement,
sale signs, and its “Everyday Low Prices” slogan. Even though one company is online, and the other is
physical, both use nudges that push customers to spend more without always realizing it. The paper also
discusses how these strategies affect consumer decision-making, what they mean for shoppers, and why
understanding them is important. Finally, it points out the limits of this research and suggests that future
studies should explore how nudges can be both helpful and harmful. Throughout this paper, the term
“digital nudges” refers to behavioral cues used in online retail settings such as Amazon, while “physical
nudges” describe those present in in-store environments such as Walmart.
| 78 |
Author(s):
Akshaj Devireddy.
Page No : 740-751
|
Recurrent Integration and the Empirical Grounding of Phenomenal Consciousness in Artificial Intelligence Systems
Abstract
Artificial intelligence systems continue to increase in sophistication, renewing the questions of
what structurally distinguishes conscious experience from computation. This paper develops a unified
framework for consciousness by combining Recurrent Processing Theory (RPT) with a weakened
form of Integrated Information Theory (IIT). The aim is to articulate a mechanistic account in which
recurrent feedback stabilizes perceptual contents and structural integration unifies them into a single,
irreducible experiential field, and then to evaluate whether contemporary AI architectures exhibit
these features. Using this framework, the analysis examines the consciousness-relevant organization
of two major classes of models: Large Language Models (LLMs) and Emergent Models (EMs). The
discussion shows that EMs, due to their intrinsically recurrent dynamics and globally interdependent
state evolution, more closely approximate the structural conditions identified by the RPT and weak IIT
account than do standard feedforward transformer-based LLMs. The paper also reconsiders the debate
between phenomenal and access consciousness by providing an RPT and weak IIT interpretation of the
Sperling experiment and by showing how EMs offer a way to render the posited structure of phenomenal
consciousness empirically tractable.
| 79 |
Author(s):
Anya Chiang.
Page No : 752-763
|
Bridging Data Scarcity in Medicine through Distribution-Driven Synthesis and Comparative Statistical Evaluation
Abstract
Reliable statistical modeling in medicine often faces a fundamental limitation: the scarcity of numerical
patient data. Ethical, logistical, and financial constraints restrict large-scale clinical data collection,
leading to small sample sizes that weaken statistical inference, inflate variance, and obscure nonlinear
relationships among physiological variables. To address this limitation, the present study employs a data
synthesis framework that expands an authentic Kaggle-sourced medical dataset of 80 patient records—
each characterized by demographic, physiological, and lifestyle attributes—into a statistically equivalent
large-sample version of 1,000 observations. Numerical variables were modeled through empirical and
Gaussian-based distributions, while categorical variables were generated via probabilistic sampling to
preserve realistic frequency structures. Comparative statistical analyses demonstrate that the synthesized
dataset closely replicates the distributional, correlational, and categorical properties of the original while
improving stability, representativeness, and parameter reliability. The enlarged dataset enhances the
detection of nonlinear and interaction effects previously obscured by sample constraints. Overall, this
study validates statistically guided data synthesis as an effective strategy for overcoming medical data
scarcity and improving the robustness of health analytics. The findings emphasize that controlled dataset
expansion can complement empirical data collection, supporting more reliable inference, generalizable
modeling, and evidence-based decision-making in quantitative biomedical research.
| 80 |
Author(s):
Rhea Agarwal.
Page No : 764-773
|
When and How We Learn Matters: Unpacking the Impact of Early Financial Literacy Education on Adult Financial Health
Abstract
The goal of this study was to investigate the impacts of the timing and method of learning financial
literacy on a person’s future financial health. There is currently a lack of financial preparedness among
teenagers and young adults worldwide. To improve the chances of a financially healthy life, it is essential
for timing, the sourcing of education, and the method of learning to align. In this study, 229 respondents
were surveyed on their financial habits. The survey included questions on budgeting, saving, emergency
funds, and investment philosophy. By creating a composite score, ranging from 0 to 100, the financial
health of respondents was analyzed. The survey’s results showed that those who learned personal
finance between the ages of 20 and 25 demonstrated the highest financial health scores. 81.6% of these
respondents were financially healthy based on their composite scores. This was especially true when
self-study or life experiences were the methods of learning. Of those who learned through self-study or
experience, 77.7% were financially healthy. Conversely, high school courses alone yielded lower scores
(M = 52.3), indicating that they may be insufficient without reinforcement through time and practice.
| 81 |
Author(s):
Morgan Theil.
Page No : 774-780
|
The Effect of Water Access on the Cognitive Development of Children Under 18: A Literature Research Review
Abstract
Globally, limited access to clean drinking water affects millions of children, especially during their
early cognitive developmental stages. This lack of reliable access is connected to dehydration, waterborne
illnesses, environmental challenges, and sociocultural barriers. A literature review was conducted using
Google Scholar and PubMed to find studies published from 2002 onward with a primary focus on children
aged zero to eighteen. Additionally, inclusion criteria included: research examining the relationship
between clean water access and cognitive abilities such as memory, language, and motor development.
Of the fourteen articles, cross-sectional studies, randomized controlled trials, and burden of disease
assessments were included. Findings showed how dehydration negatively affected cognitive abilities
including: attention, visual processing, and memory. In contrast, waterborne diseases were associated
with reduced cognitive function and growth stunting. Environmental and sociocultural factors, including
time spent collecting water rather than in school, significantly decreased children’s academic outcomes.
Inadequate water access negatively impacts early child development through biological, environmental,
and sociocultural aspects. To prevent illness and death while supporting children’s cognitive development
and academic performance, it is important to make Water, Sanitation, and Hygiene (WASH) resources
and clean drinking water accessible to everyone.
| 82 |
Author(s):
Cayden Bunjamin.
Page No : 781-790
|
Maintaining Psychological Safety in the Toyota Production System: The Role of Transparency and Decision-Making in the Age of Artificial Intelligence
Abstract
The Toyota Production System (TPS) is recognized for fostering continuous improvement and its
emphasis on team-based problem-solving within a culture that values open communication and learning.
At the same time, artificial intelligence (AI) is rapidly transforming manufacturing by introducing
opportunities for augmentation and automation. This study models how AI integration could influence
psychological safety in environments inspired by TPS principles, using synthetic data generated by
large language models (LLMs). Drawing from literature on organization learning, AI integration, and
workplace psychology, a conceptual model is developed and tested across four simulated organizational
scenarios: TPS with transparency and participative decision-making (TPS+), TPS without these
moderators (TPS−), traditional manufacturing environment with automation-based AI transparency
and participative decision-making (Non-TPS+), and a traditional manufacturing environment with
automation-based AI without these moderators (Non-TPS−). Quantitative analyses demonstrated
significant differences across conditions. Teams in the transparent and participative TPS condition (TPS+)
reported substantially higher psychological safety (M = 5.11) than those in the Non-TPS− condition (M
= 2.70), F(3, 996) = 972.56, p < .001. Similar effects emerged for team performance (M = 5.54 vs.
2.56; F(3, 996) = 1453.27, p < .001) and AI adoption (M = 4.61 vs. 3.00; F(3, 996) = 347.28, p <
.001). Chi-square analyses further confirmed significant categorical differences in pay outcomes (χ²(6) =
836.07, p < .001) and job redundancy (χ²(15) = 686.91, p Non-TPS+ > TPS− > Non-TPS−. While the data
are synthetic, the results offer preliminary support for the theoretical integration of psychological safety
and AI augmentation frameworks within human-centered production systems. Future research should
validate these trends through human-subject surveys and ethnographic case studies in real manufacturing
contexts.
| 83 |
Author(s):
Joshua Asirvatham.
Page No : 791-806
|
Classifying Alzheimer’s Disease, Parkinson’s Disease, and Control Cases Using Transfer Learning, Ensemble Learning, and Explainable AI
Abstract
Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL)
techniques have shown promise. Most DL techniques provide a robust and accurate classification of
performance. However, due to the complex architectures of the DL models, the classification results are
difficult to interpret, causing challenges for their adoption in the healthcare industry. To help improve the
adoption of AI in healthcare, this study incorporates Transfer Learning, Ensemble Learning, and XAI
techniques to propose an effective and interpretable model. This work compares the performances of
pre-trained models for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD).
Specifically, the XAI technique Saliency Map was used to overlay gradients on the MRI scan, elucidating
the regions on the MRI scan that led the model to its diagnosis. The Kaggle dataset used in the study
has three classes: Parkinson’s disease (PD), Alzheimer’s disease (AD), and control (healthy). This study
compares the performance of various pretrained models. Additionally, the diagnoses produced by the
pretrained models were ensembled to produce a final diagnosis. Combining the predictions of multiple
pretrained models can boost the performance of the model because it combines the strengths of multiple
pretrained models, achieving a higher performance than the pretrained models. The best pretrained
model EfficientNetB7 received an accuracy of 94.58% with an F1-score of 95.81%. The proposed model
of this study is the ensemble learning model with an accuracy of 97.04% and an F1-score of 97.69%.
| 84 |
Author(s):
Remon Wang.
Page No : 807-815
|
Impact of Active Aerodynamic Components on Aerodynamic Efficiency under Varying Conditions
Abstract
Global carbon emissions are at an all-time high in part due to gas vehicles. While efforts to switch to
more fuel efficient and electric vehicles are prevalent, these efforts are slow. Simple measures to reduce
aerodynamic drag through active aerodynamics would increase each individual vehicle’s efficiency,
reducing emissions. Active aerodynamic parts alter features of the vehicle’s body and its interaction
with the oncoming air. While simulations can model these effects, there is limited empirical data from
physical tests essential for understanding airflow around real components by addressing uncontrollable
variables simulations couldn’t. To address this gap, this research aims to answer the questions, To what
extent can a homemade wind tunnel demonstrate aerodynamic flow? and To what extent do active
aerodynamic components improve aerodynamic efficiency? Through an experiment involving a wind
tunnel and models of F1 rear wings with openable flaps, this paper investigates the effects of active
aerodynamics quantitatively and qualitatively. The variables of the drag force equation were measured
and experimentally determined, and the drag force of the open and closed configurations were analyzed.
Due to the reduced cross-sectional area, the configuration with an open flap exhibited a 0.51% decrease
in drag force compared to the control configuration at slow speeds. From the findings, the decrease in
drag reduces the air resistance vehicles combat, decreasing fuel consumption to achieve the same speed.
This has implications for climate change through automobile users worldwide, as the findings of this
study indicate active aerodynamics’ drag reduction directly lowers fuel consumption in vehicles.
| 85 |
Author(s):
Alexandra C. Chen.
Page No : 816-823
|
The Promise and Possibilities of Perovskite: A Comparison to Monocrystalline Solar Cells and Guidelines for the Future
Abstract
The world would be better if solar energy not only achieved high efficiency but also minimized its
environmental impact across its entire life cycle. While monocrystalline silicon (mono-Si) solar cells
dominate the market with proven efficiency and durability, and perovskite solar cells (PSCs) have surged
forward with rapid efficiency gains, there remains a gap in understanding how their global warming
potential (GWP) compares, especially given the much shorter lifespan of PSCs. This systematic review
of life cycle assessment (LCA) studies of monocrystalline silicon and perovskite solar cells using the
PRISMA framework evaluates and compares the GWP of mono-Si and PSCs in terms of gCO₂eq/kWh.
It finds that mono-Si solar cells generally range between 19–210 gCO₂eq/kWh depending on technology
and energy mix, whereas PSCs can achieve values as low as 6–12 gCO₂eq/kWh. These findings suggest
that PSCs have strong potential, but only if their lifespans can approach the durability of mono-Si cells.
| 86 |
Author(s):
Anvee Sharma.
Page No : 824-835
|
Mechanistic Insights Into HORMAD1-mediated Meiotic Regulation Using AlphaFold
Abstract
Meiosis is an essential process for gametogenesis and fertility, requiring a network of proteins that
coordinate homologous chromosome pairing, synapsis, and recombination. The HORMA domaincontaining
protein HORMAD1 plays a key role in these events by localizing to unsynapsed chromosomal
axes, promoting synaptonemal complex formation, and activating meiotic checkpoints. Disruptions
in HORMAD1 function have been linked to infertility, yet the molecular basis of its interactions and
regulation remains incompletely understood. In this study, AlphaFold was used to investigate how
HORMAD1 recognizes its own closure motif and interacts with known meiotic partners. Validation
with canonical and mutant peptides confirmed that AlphaFold accurately predicts established binding
behaviors. Screening of potential closure motif–like sequences revealed novel candidate interactions
with MCM9, SYNP2, and ATR, while IHO1 and BRCA1 showed minimal binding confidence.
Additionally, modeling of phosphorylated peptides suggested that posttranslational modification can
disrupt HORMAD1’s autoinhibitory conformation and regulate its interactions with other meiotic
factors, potentially regulating its activity during meiosis. These findings demonstrate that AlphaFold can
recapitulate known biochemical results and predict new interaction candidates, providing a structural
framework for future experimental validation. Understanding HORMAD1’s binding specificity and
regulation advances insight into the molecular mechanisms underlying meiotic control and fertility
disorders.
| 87 |
Author(s):
Jinhao Han.
Page No : 836-854
|
How Cognitive Biases Interact with Risk Perception to Influence Real Estate Investment Decisions: A Systematic Review and Discrete Choice Experiment
Abstract
Behavioral biases and risk perception shape real estate investment decisions in ways that extend
beyond the assumptions of rational choice theory. Classical Financial valuations may be logical on paper.
However, these calculations neglect the fact that finance involves different psychological dimensions
of risk perception. For example, individuals tend to underestimate losses but approach gains with
overconfidence. This paper addresses that gap by combining a systematic literature review and a highstakes
real estate choice experiment. The findings reveal that individuals exhibit risk aversions stronger
than predicted by models. It also revealed that while biases such as Loss Aversion or Herding are
significantly consequential, other biases also manifest to a different extent under environmental cues.
The choice experiment further demonstrated that intentionally nudged biased scenarios create polarized
risk preferences. Overall, the paper highlights the limitations of traditional models such as the Expected
Utility Theory (EUH) and the Capital Asset Pricing Model (CAPM) in explaining psychological drivers
in finance. By integrating behavioral insights into real estate investment analysis, a more nuanced and
interdisciplinary understanding of human behavior in the financial market would be applied, benefiting
both investors and policymakers.
| 88 |
Author(s):
Jiseok Kim.
Page No : 855-863
|
Comparison of Impact Absorption Properties Between Colloidal and Non-Colloidal Materials
Abstract
Impact absorbing materials are used in many different, industrially relevant areas, from sports to
military and defense. This review aims to address the impact absorption of two categories of materials:
colloidal and non-colloidal materials. Some model systems are considered: first is Oobleck (corn starch
and water), a well-known colloidal system that absorbs shock through shear-induced jamming via
frictional particle contacts. Second is dry cornmeal, a granular system which absorbs impact energy
through compression and friction under adequate confinement conditions. Lastly, a polymeric system
comprising polyborosiloxane (PBS), which absorbs impact through rate-dependent viscoelastic stiffening
governed by the Deborah number and temperature-dependent bond dynamics. This review compares
the shock absorbing mechanisms and performance of each of these chemically unique systems and aims
to provide criteria for selecting the suitable material for a specific application. In addition to mapping
mechanisms, practical selection rules are outlined, including when to prefer reversible stiffening (colloids
and polymers) versus irreversible compaction (granular solids), how thickness requirements and mass
penalties scale with protection level, and what environmental limits (temperature, humidity) matter in
field applications.
| 89 |
Author(s):
Arslan Maharramli.
Page No : 864-876
|
The Role of Heated Compression Therapy in Post-Exercise Recovery Across Age Groups and Sport Types
Abstract
Heated compression therapy combines two common recovery strategies—thermal therapy and
mechanical compression—to enhance circulation, reduce soreness, and restore performance. This
systematic review with a structured search (2010–2025) synthesizes evidence on compression, heat, and
their combination, and contrasts responses between younger (16–35) and middle-aged (36–54) athletes,
with additional commentary on older adults ≥ 55 where available. These broader categories were selected
due to heterogeneity in age definitions across the included studies. Compression consistently improves
venous return and perceptual recovery; heat increases local blood flow and tissue extensibility. Combined
heated compression generally yields larger short-term gains in pressure-to-pain threshold, perceived
soreness, and local perfusion than either modality alone, although protocols vary widely (temperature
38–45 °C, pressure 15–30 mmHg, 5–20 min). Younger athletes show faster vascular kinetics and quicker
readiness; middle-aged/older groups report greater reductions in stiffness and potential vascular health
benefits. Evidence is limited by small samples, male-heavy cohorts, heterogeneous devices, and labdominant
settings. Standardized protocols and outcome measurements are needed to make clearer
comparisons and real-world validity in future research.
| 90 |
Author(s):
Aiden Ashcraft.
Page No : 877-894
|
Investigating Nav1.6 and Its Potential Therapeutic Applications
Abstract
Neuropathic pain affects 6.9-10% of the global population, with first-line treatments providing
meaningful relief in only ~30% of patients. Trafficking disruption has emerged as a therapeutic strategy
for Nav1.7, where peptides disrupting the CRMP2-Nav1.7 interaction reduce channel surface expression
and alleviate pain in preclinical models. This approach remains unexplored for Nav1.6 (SCN8A), despite
regulatory protein interactions. This study evaluated whether Nav1.6 possesses molecular properties
suitable for trafficking-based therapeutic modulation. We integrated transmembrane domain prediction
(TMHMM), multiple sequence alignment across five species, protein-protein interaction network
analysis (STRING), expression profiling (GTEx), and Hodgkin-Huxley modeling of dorsal root ganglion
(DRG) neurons under varying Nav1.6 expression levels (1.0x normal, 1.5x neuropathic, 0.7x reduced, 0.1x
knockout). Analysis revealed that MAP1B binding to the Nav1.6 N-terminus prevents endocytosis and
controls surface expression, a regulatory mechanism similar to the CRMP2-Nav1.7 system, which has
been successfully targeted for Nav1.7-mediated pain. Conservation analysis showed moderate variability
in the MAP1B binding region (residues 77-80), contrasting with highly conserved transmembrane
domains. Computational modeling demonstrated that ~30% Nav1.6 reduction (0.7x expression) eliminates
repetitive firing while preserving normal responsiveness. Experimental MAP1B-Nav1.6 disruption
achieves ~40% surface expression reduction, a magnitude that falls within the therapeutic range
identified by the modeling, validating trafficking disruption as a therapeutic strategy. GTEx profiling
confirmed CNS-enriched expressions. This computational integration predicts trafficking disruption as
a therapeutic strategy for Nav1.6, with specific predictions directly testable in neuropathic pain models.
| 91 |
Author(s):
Oviya Ravi.
Page No : 895-901
|
The Role of p53 in Cell Cycle Arrest, Cellular Senescence and Apoptosis in Cells with DNA Damage
Abstract
p53, also known as the Guardian of the Genome, has a plethora of functions in cells. This review
focuses on the current understanding of p53’s roles in three mechanisms (cell cycle arrest, apoptosis,
and senescence) and how p53 determines the fate of the cell among them. Cell cycle arrest occurs when
cells temporarily halt the cell cycle in response to DNA damage, allowing time for repair. The main
way in which p53 induces cell cycle arrest is by activating the target gene p21. Apoptosis is a form of
programmed cell death that occurs if a cell is damaged beyond repair. p53 induces apoptosis through
its interaction with the BCL-2 family of proteins, such as Bax and Bak. Cellular senescence is distinct
from cell cycle arrest or apoptosis because it is an irreversible form of cell cycle arrest mediated by p53.
The current understanding is that p53 makes cell fate decisions between cell cycle arrest, apoptosis, or
senescence based on the level of DNA damage as well as the pathways it engages. This understanding
of cell fate decisions is extremely hypothetical and advancing this understanding is key to being able
to induce the various mechanisms of p53 for clinical benefit. This paper aims to investigate the cellular
pathways induced by p53 in cells that have undergone DNA damage.
| 92 |
Author(s):
Leyth Sharaf.
Page No : 902-913
|
Entrepreneurship and Inequality in Jordan: Structural Constraints and Pathways for Inclusive Growth
Abstract
Entrepreneurship is often framed as a pathway to economic advancement, yet in Jordan access to
its benefits is unevenly distributed. This study explores peer-reviewed research, national surveys,
and development reports to examine how geography, gender norms, social networks, and digital
infrastructure shape entrepreneurial opportunities across Jordan’s governorates. Using perspectives from
capital theory, embeddedness, weak ties, structural holes, and intersectionality, the analysis identifies
four structural inequalities in Jordan’s entrepreneurial landscape. These include: (i) urban capital
concentration, with Amman hosting most entrepreneurial finance and support structures; (ii) gendered
exclusion, as women’s labour force participation is below 14% and rural women’s enterprises are
typically informal and home-based; (iii) network constraints, where kinship and tribal ties provide early
legitimacy but restrict brokerage opportunities and external market access; and (iv) digital inequality,
with rural ventures disproportionately reliant on cash-only transactions due to weaker connectivity and
low e-payment uptake. These dynamics show that entrepreneurship in Jordan is not a neutral engine
of inclusion but is embedded in hierarchical spatial, social, and gendered structures. The paper argues
for decentralised entrepreneurial support organisations, gender-responsive finance, strengthened digital
infrastructure and skills, and policies that leverage informal networks while enabling entrepreneurs to
extend beyond them to avoid reproducing inequality.
| 93 |
Author(s):
Wenjin Zhao, Sara Pohland.
Page No : 914-922
|
How do Different Reinforcement Learning Optimization Models Perform in Robotic Tasks?
Abstract
Reinforcement Learning (RL) has proven to be a powerful and versatile framework for solving
complex problems. It has demonstrated success in areas ranging from game theory to robotic control to
autonomous navigation and path planning. However, the rapid development of numerous RL algorithms
has outpaced the field’s ability to provide clear, standardized comparisons, leaving practitioners with
limited guidance for selecting the most appropriate algorithm for a given task. This work addresses
this gap by conducting a systematic empirical study comparing five prominent RL algorithms–Vanilla
Policy Gradient (VPG), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization
(PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC)–across
three distinct task categories: locomotion (Humanoid), continuous control (LunarLander), and navigation
(FrozenLake). To ensure a fair comparison, we first performed a hyperparameter sweep for each
algorithm in each environment. The final evaluation, which is based on average return, sample efficiency,
and training stability, reveals that no single algorithm dominates in all domains. The key findings are that
SAC is the superior choice for complex, continuous control, achieving the highest average episode return
in both Humanoid and LunarLander; VPG performs surprisingly well in discrete, sparse-reward settings,
achieving the highest average episode return in FrozenLake; and a critical trade-off exists between peak
performance and training stability. Our findings aid practitioners in understanding the trade-offs to be
considered in RL algorithm selection for different types of robotics tasks.
| 94 |
Author(s):
Tevez Leung.
Page No : 923-943
|
Challenges In the Clinical Translation of Metal Organic Frameworks: A Review
Abstract
Metal Organic Frameworks (MOFs) are a novel class of materials that have great promise in the
biomedical field owing to their preclinical success. Despite 15 years of demonstrated success in
preclinical trials, only two hafnium-based MOFs (RiMO-301 and RiMO-401) have entered clinical
testing. This paper aims to examine this bench-to-bedside gap in MOFs with the question: What are
the key factors that contribute to the success of MOFs in preclinical studies but hinder their translation
into the clinic? Through a comparative analysis of 19 MOFs, including well studied MOF groups such
as Zeolitic imidazolate framework MOFs (ZIF) and Material Institute of Lavoisier MOFs (MIL), this
literature review will identify the advantages over conventional nanocarriers established from preclinical
testing, and the multifaceted barriers hindering clinical translation. Furthermore, it discusses systematic
strategic approaches to guide MOF research into clinical transition. By synthesizing these insights, the
paper aims to provide a roadmap for overcoming transitional bottlenecks.
| 95 |
Author(s):
Arjun Macha.
Page No : 944-951
|
Exosomal miRNAs as Biomarkers for Tissue Degeneration
Abstract
Tissue degeneration, especially cardiac tissue degeneration, is increasingly becoming a wider cause
of death. The detection of tissue degeneration is often too late, because the difficulty of detection only
becomes apparent after large damage occurs. Biomarkers are used to track the progression of diseases.
As levels elevate or decrease, they can tell us the constant development of a disease. The problem is
that normal biomarkers, such as circulating proteins, are often unusable. They degrade too easily by
enzymes, and do not provide any specificity from disease to disease. A better solution to this problem
is the use of exosomal miRNA as biomarkers. Exosomal miRNA is not only resistant to degradation
by enzymes but is also specific to a certain stressor. This allows us to track the levels of microRNA
(miRNA) in body fluids to track the progress of a disease and discerning it from other diseases. To be
able to detect changes in levels of miRNA it allows us to recognize if tissue degeneration occurs. Unlike
circulating protein biomarkers that are common throughout the body, certain types of miRNAs can
be regulated depending on where the degeneration occurs. For certain diseases, stress of cells starts a
specific pathway that either increases or decreases the production of a specific miRNA. These pathways
are regulated by proteins that bind to miRNA and guide them to secretion.
| 96 |
Author(s):
Sri Medha Pedaprolu.
Page No : 952-966
|
Thermal Imaging for Concealed Weapon Detection using Computer Vision
Abstract
The recent increase in gun violence, especially in underprivileged communities, highlights the need
for affordable concealed weapon detection to enhance public safety. While there are existing commercial
solutions, such as millimeter wavelength imaging, they are expensive and may not be easily deployed
in under-resourced communities. Thermal imaging can be used to identify concealed firearms through
heat pattern analysis, highlighting hidden metallic objects based on temperature contrast. This study
investigated four classification models: CNN, MobileNetV2 Transfer Learning model, YOLOv8
Classification, and a Vision Transformer, and one object detection model (YOLOv8), to compare their
effectiveness on the Concealed Pistol Detection Dataset. The classification models were evaluated
based on accuracy, precision, recall, and F1-scores. Among them, the YOLOv8 and Vision Transformer
both achieved accuracy exceeding 96%, with the Vision Transformer appearing to be slightly more
conservative when classifying images as ‘withgun’. The MobileNetV2 Transfer Learning model and
CNN also performed extremely well, with accuracies exceeding 0.94. The detection models (both
before and after augmentation) were evaluated based on precision, recall, mAP50, and mAP50-95, and
the model without data augmentation performed better for concealed weapon detection for the dataset.
The YOLOv8 models can be executed on a Raspberry Pi, while the Transformer model may require
additional computing power, such as the NVIDIA Jetson Nano.
| 97 |
Author(s):
Ian Andrefski, Mehul Mehezabin.
Page No : 967-973
|
Breathing Easy: Cystic Fibrosis, CFTR Modulators, and the Economic Struggle for Access
Abstract
Cystic fibrosis (CF) is a genetic disease caused by mutations to the cystic fibrosis transmembrane
conductance regulator (CFTR) protein, which causes an inability in affected cells to properly move
chloride (Cl-) and bicarbonate (HCO3
-) across epithelial tissue, leading to symptoms such as abdominal
distress and the presence of thick mucus in the lungs and airways that collect bacteria and contribute
significantly to CF patient mortality. Healthcare providers have developed treatment plans designed to
detect CF early and treat its symptoms. CFTR modulators such as ivacaftor, elizacaftor, and tezacaftor
offer patients the ability to correct the mutations in the CFTR protein and remove symptoms entirely.
However, these medications are expensive, contributing to healthcare inequality and outcomes for CF
patients. To promote healthcare accessibility, Vertex Pharmaceuticals, either voluntarily or through
government-mandated compulsory licensing in affected countries, could allow other companies to
produce generic options at a fraction of the existing price, thereby removing economic barriers and
promoting healthcare accessibility for CF patients worldwide. This review analyzes the effectiveness of
CFTR modulators and how that effectiveness is counterbalanced by economic and ethical disparities,
which make them inaccessible to much of the general population worldwide.
| 98 |
Author(s):
Mark Mu Lowe.
Page No : 974-984
|
Evaluating U.S., EU, and UN Food Aid in Yemen and South Sudan (2020–2024) Against Sphere Standards
Abstract
Humanitarian food assistance faces renewed scrutiny as acute hunger reaches its highest global level
in decades. This study evaluates how emergency aid financed by the United States and the European
Union, and coordinated by the United Nations, in Yemen and South Sudan (2020–2024) aligned with
Sphere Handbook standards for daily calories (2,100 kcal), protein share (10–12%), and micronutrient
adequacy. Drawing on Integrated Food Security Phase Classification data, agency monitoring records,
financial trackers, and peer-reviewed efficiency studies, the research applies a quantitative and qualitative
comparative design to quantify delivery shortfalls and diagnose systemic constraints. Findings show that
large-scale operations averted famine for more than 20 million people, yet provided only 1,050–1,500
kcal on average; protein goals were inconsistently met, while micronutrient diversity remained scarce,
sustaining crisis-level malnutrition. Six interlocking barriers explain these gaps: persistent funding
deficits, limited efficiency gains from cash and local procurement, uneven accountability and targeting,
politicized access restrictions, the under-prioritization of nutrient-dense commodities, and the limited
scale of forecast-based anticipatory financing. The paper concludes that multi-year flexible funding,
mandatory nutrition metrics, unified access diplomacy, scaled biometric oversight, and mainstream
anticipatory action could markedly raise the marginal social benefit of each humanitarian dollar and shift
food aid from triage toward standards-based nutrition security.
| 99 |
Author(s):
Godghate Shantilp Sushant.
Page No : 985-994
|
Ethanol Blend Fuels in Internal Combustion Engines: Effects on Performance, Fuel Stability, and Emissions: A Review
Abstract
Currently, internal combustion engines (ICE) utilize gasoline-based fuels to generate energy. These
fuels have the tendency to emit products of incomplete combustion, as well as toxic and greenhouse
gases. Ethanol can be used as an additive to gasoline fuels to limit these downsides. The objective
of this study is to evaluate the impact of ethanol–gasoline blends on engine performance, emissions,
corrosion, and blend stability. To evaluate whether the addition of ethanol is beneficial, the impacts of
the alcohol on engine performance, engine emissions, blend stability, and corrosion were reviewed. This
review indicates increases in power, torque, and efficiency by 5%, 3%, and 2% respectively; decrease in
hydrocarbon (HC) and carbon monoxide (CO) emissions by roughly 30% and 40% respectively; increase
in nitrogen oxides (NOX) and carbon dioxide (CO2) emissions by roughly 50% and 5% respectively;
and mixed results for toxic emissions. These results were all compared to the data from base gasoline.
It is also noted that temperature is a major factor for the stability of ethanol-gasoline blends and that
corrosive properties of the fuel depend on the fuel’s water content.
| 100 |
Author(s):
Victor Stanoev.
Page No : 995-1012
|
The Paradox of Education: Why Highly Educated Bulgarian Women Still Earn Less than Men in Bulgaria
Abstract
This article argues that the gender pay gap (GPG) in Bulgaria is driven primarily by structural and
institutional factors rather than individual-level choices. Drawing on descriptive statistical evidence, it
contends that weak enforcement, limited political representation of women, legislative shortcomings,
and gendered barriers in entrepreneurship collectively sustain the gap. The analysis highlights that
women’s underrepresentation in Parliament, where they occupy only about one quarter of seats and
where no gender-quota mechanisms exist, reduces the likelihood of advancing equal-pay legislation,
childcare reforms, and pay-transparency measures. At the same time, men’s greater access to high-risk,
high-reward entrepreneurial opportunities, supported by more favorable lending conditions and more
forgiving second-chance rules, may result in income distributions that disproportionately benefit men.
These structural dynamics, taken together, help explain why Bulgaria continues to display one of the
wider gender pay gaps in the European Union despite formal legal protections. The article concludes
by outlining policy implications related to institutional enforcement, political representation, pay
transparency, and support for women-led enterprises, and calls for future empirical research to more
precisely quantify each mechanism’s contribution to Bulgaria’s GPG.
| 101 |
Author(s):
Leslie Gao.
Page No : 1013-1019
|
Mimicking Nature to Replace Fossil Fuels: The Promise of Artificial Photosynthesis Systems
Abstract
Rapid anthropogenic warming has accelerated climate risks beyond historical precedent,
underscoring the necessity of replacing fossil fuels, which account for nearly 90% of global CO₂
emissions. This perspective argues that artificial photosynthesis, particularly the Si–perovskite tandem
photoelectrochemical (PEC) system, represents the most promising pathway for large-scale energysystem
decarbonization. Recent breakthroughs enabling sunlight-driven conversion of CO₂ into
multicarbon liquid fuels mark a pivotal advance, offering storable, energy-dense alternatives uniquely
suited for hard-to-electrify sectors such as aviation, shipping, and heavy industry. The Si–perovskite
tandem PEC overcomes long-standing challenges in photovoltage, light absorption, and material cost,
and emerging solutions such as dual-skin ALD/SLIPS coatings show potential to address the critical
instability of perovskites. However, translating laboratory success into global adoption requires targeted
policy support, including early-stage capital subsidies, stringent recycling regulations for lead-based
perovskites, expanded R&D funding, and international frameworks ensuring equitable technology
transfer. Efficiency estimates and energy-supply projections suggest that artificial photosynthesis could
meaningfully contribute to global fuel demand while reducing lifecycle emissions, though the paper
acknowledges uncertainties related to land use, intermittency, and commercial scalability. Alternative
decarbonization pathways, such as green hydrogen, offer valuable context but face infrastructure
and energy-density limitations. Ultimately, this paper contends that with coordinated technological
innovation and policy action, artificial photosynthesis can become a cornerstone of a just, scalable, and
carbon-neutral global energy system.
| 102 |
Author(s):
Rishi Pudipeddi.
Page No : 1020-1026
|
A Review of Control Algorithms on Compute-Constrained Remotely Operated Vehicles
Abstract
Small underwater robots must hold depth, attitude, and position in the face of strong disturbances while
running on limited onboard processors. To inform controller choice under these constraints, this study
conducted a quantitative analysis of closed-loop controllers applied to ROVs with a qualitative analysis
of the feasibility of running these controllers on different compute platforms. Evidence was drawn from
comparative studies that evaluated multiple controllers within the same specific experimental setup, and
the performance of controllers was assessed by computing the win-rates of controller families under the
same experimental conditions. Controllers were grouped under the families PID, Fuzzy, Optimal, SMC,
and MPC. The findings outline clear performance patterns and practical compute tradeoffs, offering
guidance for choosing controllers under embedded constraints while highlighting where further head-tohead
testing would be most informative.
| 103 |
Author(s):
Aarav George.
Page No : 1027-1035
|
Harnessing Vehicle Aerodynamic Losses: A Computational Study of Vortex-Induced Piezoelectric Energy Generation for Roadside Applications
Abstract
Road vehicles lose significant energy through aerodynamic drag, creating turbulent wakes with
energy-harvesting potential. This study investigates using vehicle-induced vortex shedding to excite
piezoelectric cantilever beams for roadside power generation. Computational fluid dynamics simulations
with OpenFOAM model flow past a cylindrical bluff body representing a vehicle feature, with a
downstream flexible beam located in the wake. The cylinder produces a von Kármán vortex street that
generates alternating pressure loads on the beam. When piezoelectric layers are bonded to the beam,
this cyclic bending can convert otherwise wasted aerodynamic energy into electrical power for lighting,
sensing, or traffic-monitoring equipment. Baseline simulations confirm stable, periodic vortex shedding
and capture the associated unsteady pressure fields in the wake region. However, comparison of the
numerically obtained shedding frequency with values predicted from the Strouhal relationship reveals
an order-of-magnitude discrepancy, indicating that additional refinement of mesh resolution, temporal
discretization, and boundary conditions is required before quantitative power estimates can be made.
Despite these limitations, the results demonstrate that vehicle-generated wakes can provide the cyclic
aerodynamic loading needed to drive piezoelectric harvesters. The study establishes a CFD framework
for analyzing this coupling and outlines key modeling improvements and experimental validation steps
required to scale the concept to realistic vehicle-wake velocities and to design practical, low-maintenance
energy-harvesting modules for sustainable roadside infrastructure.
| 104 |
Author(s):
Siwoo Jeong.
Page No : 1036-1044
|
Comparative Historical Analysis of Public Health System Transformation in South Korea and the United States (1918-Present)
Abstract
The public health systems of South Korea and the United States developed under different historical,
political, and cultural factors, which have produced distinct strengths and weaknesses during health
crises. The research examined the system structures, pandemic responses, and long-term health outcomes
of both countries from 1918 to the present, using historical case studies and policy analysis. South
Korea achieved quick insurance expansion and effective pandemic management through centralized
governance, collectivist culture, and its post-MERS reforms, which led to high public compliance. The
United States adopted a decentralized, market-based system based on individualism, which resulted in
delayed coordination and elevated COVID-19 death rates, despite its higher healthcare expenditures. The
research demonstrated that emergency preparedness depends equally on governance structure, cultural
values, and public trust as it does on funding and technology. The study recommended that the United
States should improve its national coordination systems and that Korea should enhance equity in the
hospital sector while implementing reforms that balance efficiency with inclusivity. The study of how
historical and cultural elements shape public health systems offered valuable insights to strengthen crisis
readiness for future global health emergencies.
| 105 |
Author(s):
Maxime Goulet.
Page No : 1045-1052
|
Calcium Channel Blockers in Cardiac Surgery: Efficacy, Administration, and Clinical Outcomes in Arterial Graft Management
Abstract
Calcium channel blockers (CCBs) have emerged as important therapeutic agents in cardiac surgery,
particularly for preventing arterial graft spasm during coronary artery bypass grafting (CABG). This
literature review synthesizes evidence on CCB efficacy, administration methods, and clinical outcomes in
cardiac surgery patients. A comprehensive analysis of 25 studies spanning 1987-2025 reveals consistent
short-term benefits of CCB therapy in preventing arterial graft spasm and improving immediate surgical
outcomes. CCBs, including diltiazem, verapamil, nicardipine, and amlodipine, show robust short-term
efficacy in improving graft flow rates and reducing perioperative complications, with emerging agents
like efonidipine offering novel dual-channel blocking activity. While observational studies suggest
sustained benefits, randomized controlled trials question prolonged treatment necessity. Therefore, the
long-term effectiveness of these medications remains controversial. Critical gaps include insufficient
large-scale randomized trials, unclear optimal treatment duration, and limited patient stratification
research. Future investigations should focus on multi-center randomized controlled trials, novel dualchannel
blocking agents, and personalized treatment approaches based on patient-specific risk factors.
| 106 |
Author(s):
Rachel R. Q. Zeng.
Page No : 1053-1062
|
Impact of Non-Specific Fine Motor Training on Intermanual Transfer Learning
Abstract
Humans perform better at dexterity tasks with their dominant hand, but can training in one hand
affect the performance of the other? This study examines the impact of increased non-dominant hand
usage in everyday life tasks on intermanual transfer learning. I conducted weekly drawing tests of 14
research participants split into groups that either practiced or did not practice non-dominant hand use
between testing. Aiming to lead to a better understanding of the scale and confines human dynamics
pose during the process of intermanual transfer learning, the experiment yielded the following results: (1)
general, not-task-specific short-term practice improved fine motor skill performance by an average of 1%
for group A right-hand (R) accuracy and an increase of 2% for left-hand (L) performance after the first
training phase with p-values of 0.61 and 0.23 respectively, which suggests no statistical significance. (2)
Short-term non-dominant-hand practice by conducting an increased number of everyday tasks shows a
possible close relationship between the factors of time, practice, and side, suggested by FTimeSideGroup
= 2.53 and ηp² = 0.17, which as of now lack greater statistic power with p = 0.14. (3) Reducing practice
with the non-dominant hand resulted in reduced performance quality, which is visible in the average drop
of accuracy by 1% of group A subjects after ceasing training with the non-dominant hand for a week.
The connectivity between the factors of the interaction between time, side, and practice can also be
observed in the decrease of performance quality when regarding F = 3.95 and ηp² = 0.25 with p = 0.07,
suggesting lack of statistical relevance. Summing up, though discovering possible impact of training on
L and R performance accuracy after a short amount of time, these research results are not sufficient in
statistical power to certain a noticeable impact of non-specific practice on cross-hand education. The
experiment design, nevertheless, established a paradigm for studying intermanual transfer learning in a
school setting and identified methods and limitations to assess drawing skills for quantitative analysis for
future research.
| 107 |
Author(s):
Palash Gupta.
Page No : 1063-1075
|
Overcoming Deficiencies in Gene Therapy Delivery with Chimeric and Inducible Vectors
Abstract
Viral gene therapy intends to bring genetic materials to the cell using a modified virus. This delivery
system is called a viral vector. The viral vector often takes the property of the virus in regards to
delivery, so the choice of virus leads to various outputs. Viruses differ from one another depending on
their tropism, hitting certain tissues better than others; immunogenicity; and size. This review compiles
evidence of recent innovations in the delivery of gene therapy using viral vectors with a specific emphasis
on inducible and chimeric viral vectors that address long-standing limitations of traditional viral vectors.
Conventional viral vectors, despite being the current standard for gene therapy delivery, pose risks
and have significant flaws. Many elicit severe immunogenic responses and often hit unintended cells
and tissues. This review discusses two new categories of viral vectors: chimeric vectors, which are a
combination of two or more vectors, and inducible viral vectors, which use spatio-temporal awareness
to improve efficiency and targeting. Chimeric vectors adopt key features from multiple different vectors
to create a hybrid. These hybrid vectors can target new cell types, lower immunogenicity, and make
downstream purification easier. Meanwhile, inducible vectors rely on external stimuli to dictate their
expression. Inducers include small molecules, RNA, and light. While concerns remain around scalability
of these therapies, the next level targeting capabilities make them promising for further use in the gene
therapy space.
| 108 |
Author(s):
Jiabei Chen .
Page No : 1076-1085
|
Sleep Shame in Adolescents: Mechanisms, Measurement, and School-Level Implications
Abstract
Adolescent sleep health has become a critical public health and educational concern globally,
particularly among Chinese adolescents facing intense academic pressure—an issue linked to impaired
attention, memory, and emotional regulation. This study aims to fill these gaps by defining sleep shame,
developing a measurement tool, and examining its mechanisms and implications among Chinese
adolescents. Using a mixed-methods design, we synthesized quantitative data from a survey of 2,022
Chinese adolescents and qualitative insights from 25 semi-structured interviews with students, parents,
and teachers. Key quantitative findings reveal that sleep shame manifests in four core dimensions:
moral anxiety, self-denial, social comparison, and concealment. Additionally, 39% linked rest to “lack
of diligence” and 27% equated more sleep with “failure.” Statistical analyses confirm that academic
pressure is positively correlated with sleep shame, sleep shame is associated with shorter sleep duration
and poorer subjective sleep quality, and sleep shame partially mediates the relationship between academic
pressure and sleep outcomes. Qualitative findings further identify systemic roots, including cultural
narratives celebrating diligence, competitive educational settings, and intergenerational transmission of
“less rest = hard work” beliefs. The study concludes that sleep shame is a socio-cultural phenomenon
rather than an individual issue. Effective interventions must move beyond individual-focused tools and
adopt a holistic “environment-culture-service” model to reconstruct a societal culture that values rest
as a prerequisite for well-being and productivity. These findings provide a conceptual framework and
measurement tool for sleep shame, offering actionable insights for educators, families, and policymakers
to support adolescent sleep health.
| 109 |
Author(s):
Zahra Ahmad.
Page No : 1086-1094
|
Barriers to Reproductive Health Access for Women in Northern Syria During the Civil War: A Multi-Dimensional Analysis
Abstract
Women in Northern Syria face persistent barriers to reproductive health access amid the ongoing
conflict. This paper employs a qualitative synthesis of peer-reviewed studies, NGO assessments, and
humanitarian data to examine the multidimensional constraints shaping women’s reproductive health
outcomes. Findings reveal seven intersecting barriers: attacks on health infrastructure and personnel,
displacement, economic hardship, limited donor funding, restrictive gender norms, lack of awareness,
and weak institutional coordination. The deliberate targeting of hospitals has disrupted service delivery,
while inflation and poverty have rendered care unaffordable. Social restrictions and misinformation
further restrict women’s autonomy and access to accurate reproductive information. Together, these
barriers compound vulnerability and perpetuate cycles of inequity. The study concludes that reproductive
health access must be prioritized within post-conflict recovery agendas, emphasizing the necessity of
sustained international aid, gender-responsive policy reform, and long-term investment in healthcare
infrastructure, as prerequisites for rebuilding stable and equitable Syrian communities.
| 110 |
Author(s):
Riya Rajesh.
Page No : 1095-1101
|
Impact of Motor Symptoms on Oral Hygiene in Parkinson’s D isease Patients
Abstract
Currently, over 1.1 million Americans are fighting a daily battle with Parkinson’s disease (PD). PD is
the second most common neurodegenerative disorder characterized by motor and nonmotor symptoms.
In the United States, the economic burden of the disease is estimated to be $52 billion annually.
Those numbers indicate the financial costs a society or individual incurs due to direct healthcare costs
(medications and hospital visits) and indirect costs (expenses for caregivers and wages lost due to
inability to work). Oral health is an essential aspect for high quality of life, but PD patients are at a
disadvantage when it comes to maintaining their oral health. However, despite its importance, very few
studies have investigated oral health in PD patients, largely overlooking this aspect of their well-being.
This article examines how PD motor symptoms affect oral hygiene in patients. To obtain evidence for the
effects of PD symptoms on oral hygiene, four studies were reviewed. These studies collectively show that
oral health is compromised in PD patients with individuals facing difficulty chewing and swallowing,
carrying out routine practices such as tooth brushing, and accessing dental care.