| 1 |
Author(s):
Aleena Shahab Khokhar.
Page No : 1-8
|
How have Biomedical Engineering Advancements Shaped the Development of Contraceptive Devices from the 19th Century to the Present?
Abstract
Contraception has undergone significant transformation throughout the years, evolving from
rudimentary trial-and-error methods to contemporary bio-integrated technologies that shape
reproductive autonomy and global health. This review traces the evolution from initial barrier methods
and intrauterine devices (IUDs) to hormone delivery systems, with the emergence of implants, vaginal
rings, and advanced contraceptive technologies. Analyzed from medical, technological, and social
perspectives, it illustrates how scientific innovation, improved healthcare infrastructure, and supportive
policies have expanded access to family planning while influencing individual future aspirations. In the
future, emerging technologies such as multipurpose preventative technologies (MPTs), bioinformatics,
and artificial intelligence (AI) may enhance the personalization, equity, and accessibility of birth control.
Ultimately, the history of birth control reflects not only biomedical advances but also the ongoing global
struggle for gender equality, reproductive rights, and health equity.
| 2 |
Author(s):
Diya Chopra.
Page No : 9-13
|
A Narrative Review of Conflict Responses in Children With and Without Clinically Diagnosed OCD
Abstract
This narrative review discusses the academic and social difficulties associated with attending school
as a child diagnosed with Obsessive Compulsive Disorder (OCD). Drawing from existing research on
school aged children and their responses to conflict, this review organizes findings based on topics that
examine social, emotional, and academic environments and their effects on children with and without
OCD. Four themes emerged: an overview of peer relationships, conflict triggers, behavioral responses,
and school experiences. Findings indicate that social and emotional challenges can impact academic
outcomes in children with OCD, and behavioral impairments can impact the school experience.
Synthesizing these findings highlights the importance of understanding the patterns of OCD to provide
research to better support children with OCD in school environments.
| 3 |
Author(s):
Alexander Liu.
Page No : 14-18
|
Proof‑of‑Concept Machine Learning Classifier for Identifying Clouds and Haze in Exoplanet Transmission Spectra
Abstract
Traditional methods for determining whether an exoplanet atmosphere is clear, cloudy, or hazy can
require substantial manual interpretation of transmission spectra. Here, a proof‑of‑concept machine
learning (ML) classifier is developed to assess whether synthetic training data can support automated
classification of atmospheric conditions in observed spectra. A dataset of 10800 synthetic spectra was
generated using petitRADTRANS, spanning three classes (clear, cloudy, and hazy). The dataset was split
into training, validation, and testing sets (70/15/15 percent), and a random forest classifier was trained and
evaluated. The model achieved testing accuracy of approximately 99–100 percent, with cross‑validated
F1 scores above 0.98 across all classes. The trained model was then applied to five observed exoplanet
spectra and produced cloudy classifications with probabilities between 65 and 89 percent. Although the
small sample size and synthetic training data limit generalizability, this study demonstrates the potential
for ML to accelerate atmospheric characterization workflows. Future work with larger and more diverse
datasets will be required to validate the method for broader scientific applications.
| 4 |
Author(s):
Seungyoon Shin.
Page No : 19-25
|
A Cross-Sectional Analysis of Urban Air Pollution and Modeled Health-Economic Burden
Abstract
This study performs a cross-sectional analysis of global urban air pollution and health-economic
burden modeled by using publicly available World Air Quality Index Data that contain 16,695 records
from 14,229 city-county pairs across 185 countries. EPA breakpoint interpolation was used to estimate
PM2.5 concentrations from AQI values and summarize them at city and county levels. To estimate
relative risk (RR), attributable fraction (AF), and normalized economic burden were estimated by
applying a simplified concentration-response model (β corresponding to a 6% risk increase per 10μg/
m3) by using standardized population and value-of-statistical-life parameters. Mean inferred PM2.5
concentrations in many cities substantially exceeded the WHO guideline of 5μg/m3 and varied by more
than an order of magnitude across cities. Cities with higher PM2.5 concentrations showed proportionally
higher modeled RR and AF, which were associated with markedly larger normalized economic burden
estimates. Without estimating causal effects or absolute costs, this study demonstrates how a transparent
and reproducible quantitative framework may reveal global disparities in pollution-related health burden
by using open data. The quantitative framework proposed in this study provides a foundation for future
studies to incorporate temporal, demographic, and policy variables.
| 5 |
Author(s):
Elliot McGuire, Rui Rebich Hespanha, Joao Hespanha.
Page No : 26-34
|
Analysis of the Accuracy and Efficiency of Neural Networks to Simulate Navier-Stokes Fluid Flows with Obstacles
Abstract
Conventional fluid simulations can be time consuming and energy intensive. We researched
the viability of a neural network for simulating incompressible fluids in a randomized obstacleheavy
environment, as an alternative to the numerical simulation of the Navier-Stokes equation. We
hypothesized that the neural network predictions would have a relatively low error for simulations over
a small number of time steps, but errors would eventually accumulate to the point that the output would
become very noisy. Over a rich set of obstacle configurations, we achieved a root-mean-square-error
of 0.32% on our training dataset and 0.36% on a testing dataset. These errors only grew to 1.45% and
2.34% at time step t = 10 and, 2 .11% and 4 .16% at time step t = 20. We also found that an
accurate
neural network can be approximately 8,800 times faster at predicting the flow than a conventional
simulation. These findings indicate that neural networks can be extremely useful at simulating fluids in
obstacle-heavy environments. Useful applications include modeling forest fire smoke, pipe fluid flow,
and underwater/flood currents.
| 6 |
Author(s):
Zihan Liu.
Page No : 35-45
|
Theories of Distributional Models, Embodied Cognition and Hybrid Approaches on Acquisition of Abstract Concepts in Human Cognition
Abstract
Understanding human comprehension of the origin and purpose of lexical words beyond their mere
sensory features (e.g. how they look when they are written or how they sound when they are pronounced),
also known as the “symbol grounding problem” remains one of the most profound challenges in cognitive
sciences. It is particularly challenging for abstract concepts because they lack tangible references in
the surrounding world for intuitive and universal understanding of their true meaning. In cognitive
science, there are two major theories: the distributional model and embodied cognition model. However,
the distributional model struggles with how words become meaningful, and the embodied approach
is limited in explaining highly abstract ideas. Since both major approaches have critical gaps in their
explanations of abstract concept comprehension, an alternative approach is needed to bridge these gaps
and provide a more comprehensive understanding of human concept acquisition. This review examines
how the distributional model, the embodied approach, and the hybrid model explain abstract concept
acquisition, by referencing relevant empirical evidence. The review also discusses the strengths and
limitations of both models, explaining why it is important to adopt an alternative approach.
| 7 |
Author(s):
Riddhi Bhashkar, Omar Tawakol.
Page No : 46-51
|
Decoding the Direction of Arm Movements using Long Short-Term Memory and Poisson State Space Model Algorithms
Abstract
Brain-computer interfaces (BCIs) allow for decoding neural signals associated with intended
movements and converting that to commands that can control external hardware, including prosthetics.
This can help individuals with paralysis who lack the ability to voluntarily control their limb movements.
This study aims to investigate decoding the direction of arm movement from single-neuron non-human
primate recordings obtained during a center-out reaching task. The intended direction of movement
was decoded from a population of directionally tuned neurons using two decoding algorithms: Long
Short-Term Memory (LSTM) and another algorithm known as the Poisson State Space Model (POSSM).
LSTM learns spike patterns directly from the data, while POSSM uses a framework specifically tuned to
neuronal firing dynamics. The results demonstrated that the POSSM achieved relatively higher decoding
accuracy when compared to LSTM, suggesting that the neural activity in the dataset follows patterns
suited to the Poisson model’s assumptions. POSSM is a relatively novel algorithm, and this manuscript
represents an early attempt to adopt it in a motor decoding task. The results suggest that it outperforms
LSTM. These findings may inform the design of more accurate and efficient brain–computer interface
systems and support future advancements in motor rehabilitation strategies.
| 8 |
Author(s):
Oh, Seungheon.
Page No : 52-62
|
Health Impacts of Environmental Stress: Case Studies of Fire, Oil Spills, and Air Pollution
Abstract
Environmental stressors impose substantial population-level health burdens and can contribute to
increased disability-adjusted life year (DALY) burden and reduced quality of life. This paper examines
short- and long-term health impacts of three major environmental stressors: wildfire smoke, marine oil
spills, and chronic air pollution from coal-fired power plants. Using a narrative literature review, the study
utilizes epidemiologic, toxicologic, and health systems evidence from various case studies, including the
2020 and 2025 California wildfire seasons, the 2007 Hebei Spirit oil spill in Taean, South Korea, and
population level exposure to coal related fine particulate matter (PM₂.₅). Amongst these three stressors,
exposure to complex mixtures of particulate matter, volatile organic compounds (VOCs), polycyclic
aromatic hydrocarbons, and combustion related pollutants was consistently associated with increased
respiratory, cardiovascular, neurologic, and psychological morbidity. Evidence from longitudinal cohort
studies and burden-of-disease analyses suggests that these impacts may extend beyond acute exposure
periods and be associated with prolonged healthcare utilization, chronic disease risk, and mental health
distress. For the Hebei Spirit oil spill case study, a burden-of-disease analysis estimated approximately
14,724 DALYs attributable to the spill in 2008 (5). Analysis of healthcare utilization following the Hebei
Spirit oil spill illustrates significant specialty-specific disruptions and patterns of delayed recovery,
reflecting both toxic exposure pathways and broader social and healthcare system stress. Environmental
disasters and chronic pollution may function as important contributors to lasting population-level health
burden. Integrating environmental exposure assessment into public health assessments and strengthening
preventive policies are essential to mitigating the long-term consequences of increasingly frequent
environmental stress incidents.
| 9 |
Author(s):
Victoria Su.
Page No : 63-74
|
Modeling Dual Treatment Outcomes in Mental Health: A Joint Statistical Framework for Discrete and Ordinal Responses
Abstract
Mental health disorders continue to be the leading cause of declining well-being, day-to-day function,
and overall quality of life for people of all ages. Understanding the factors that shape treatment progress
and long-term recovery is essential for improving clinical decision-making and personalized patient care.
This study uses a sequence of quantitative models, including ordered logit, multinomial logit, and a joint
correlated framework, to analyze how demographic, behavioral, and other treatment-related variables
influence both intermediate and final recovery trajectories. The Kaggle dataset comprises 500 patient
observations with comprehensive information on demographics, diagnoses, medication types, therapy
modalities, and adherence to their treatment plan. The results indicate that sleep quality has a significant
impact on treatment progress, while age and symptom severity are the strongest determinants of the
outcome of therapy. The joint model identifies a weak correlation between intermediate progress and
long-term improvement. Overall, the findings indicate that lifestyle factors, such as sleep quality and
recovery behaviors, play an important role in short-term therapeutic success, while demographic and
clinical factors primarily influence the outcome of therapy.
| 10 |
Author(s):
Shreya Karthik.
Page No : 75-89
|
Investigating Correlations Between Functional Connectivity Disruption And Social Behavior Difficulties In Autism Spectrum Disorder
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social
behavior. Emerging evidence indicates that those with ASD exhibit disruption in neural networks
supporting social behavior. This study investigated group and individual brain differences to identify the
neural underpinnings of ASD. Resting-state functional magnetic resonance imaging data was obtained
from the Autism Brain Imaging Data Exchange Initiative to perform whole-brain functional connectivity
(FC) analysis, comparing ASD-affected brains to brains with typical development. Results showed lower
FC in the ASD group among the following regions: left superior frontal gyrus, anterior cingulate gyrus,
right posterior parahippocampal gyrus, left Heschl’s gyrus, right planum polare, left and right precentral
and right postcentral gyrus, left supracalcarine cortex, cuneus, right occipital pole, and cerebellum regions
(vermis). These FC values were compared to measures of social behavior (the Social Responsiveness
Scale (SRS) and the Social Communication Questionnaire (SCQ)). A negative correlation was found
between the SRS awareness subscale score and connectivity of the right parahippocampal gyrus with
the superior frontal gyrus, and a positive correlation was found between the SRS Mannerisms subscale
and connectivity of Heschl’s gyrus with the cerebellar vermis 1 2. Additionally, the SRS Cognition
and Communication subscale scores were positively correlated to the connectivity of the left Heschl’s
gyrus with vermis 1 2. These results can be used to understand the neurological basis of ASD and
determine an objective identification of ASD and the severity of social behavior impairment, leading to
the development of effective individualized treatments for a better quality of life.
| 11 |
Author(s):
Abhishek Desai.
Page No : 90-101
|
Feasibility and Parametric Analysis of Nuclear Thermal Propulsion for Crewed Earth-Mars Transfers
Abstract
Colonizing Mars is considered an important objective for long-term human exploration, scientific
discovery, and technological advancement. However, crewed missions to Mars using chemical propulsion
take around 7-10 months at a minimum, posing significant challenges for crew health and mission
reliability. One promising solution to reduce this travel time and still maintain feasibility is by employing
Nuclear Thermal Propulsion, or NTP. This study simulates an Earth-Mars transfer, using circular and
coplanar orbital mechanics as well as patched-conic and two-body approximations, to evaluate the
effectiveness of NTP in reducing travel times to Mars. These results show that NTP can feasibly reduce
travel times to 70-90 days during the optimal launch window while achieving a hyperbolic excess
velocity, or V-infinity of 10 km/s. This result considers the trajectory and launch window of such a
mission, the propellant mass fractions of the rocket, travel time, and reserve propellant. These findings
indicate that NTP represents a technically realistic and advantageous option for future crewed missions
to Mars, and eventually, the rest of the Solar System. NTP offers a significant reduction in transit time
when compared to chemical propulsion but remains consistent with current reactor technology and
demonstrated technological advancements.
| 12 |
Author(s):
David Shif.
Page No : 102-109
|
Virtual Reality Technology as an Intervention for Autism Spectrum Disorder: A Narrative Review
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is often associated with
ongoing attention deficits, lack of inhibitory control, dysfunction in executive functioning, and poor
classroom behavior. These are all necessary for adaptive functioning in everyday life, but it is quite
challenging to improve them through traditional methods. Virtual reality (VR) is a newer option as it
can produce realistic, repeatable tasks in an engaging, interactive format that may be a neurodivergent
individual’s preferred way of working. This narrative review aims to determine whether immersive or
semi-immersive VR interventions can lead to substantial improvements in core cognitive and behavioral
symptoms of children and adolescents with ASD. For this narrative review, evidence was synthesized
from 12 peer-reviewed studies examining VR interventions in ASD populations. The findings indicate
that VR-based interventions are consistently effective in improving attention and inhibitory control, with
small to moderate short-term gains being reported in several studies. On the other hand, the findings
on executive functioning and working memory are mixed, and the results for classroom behavior and
daily functioning are promising within VR environments. Still, there is limited evidence of reliable
transfer to real-world settings. The common limitations across the research are small sample sizes, short
intervention durations, and inconsistent outcome reporting. Nevertheless, VR seems to be a safe and
engaging complementary tool that can facilitate cognitive development in ASD when it is supervised and
individualized.
| 13 |
Author(s):
Kacey Song, Hyoju Kim, Audrey Lee.
Page No : 110-114
|
Solubilization of Benzyl Acetate by Polysorbate 80: A Thin Layer Chromatography Study
Abstract
In today’s society, we are exposed to many chemicals, either natural or synthetic, which can cause
allergic reactions on the skin. To investigate the solubilization of benzyl acetate, which can cause
allergic reactions in high concentrations, we decided to use Polysorbate 80 owing to its availability,
cost-effectiveness, and versatile applicability as a surfactant. In this paper, we tested two methods: one
is to observe the minimum amount of Polysorbate 80 required to fully dissolve different concentrations
of benzyl acetate in an aqueous solution. The other experiment was conducted to study the interaction
between Polysorbate 80 and benzyl acetate using thin-layer chromatography (TLC) and spotting trials.
Polysorbate 80’s durability is exemplified by how difficult it is to separate mixtures that contain benzyl
acetate. In two different experiments, the amount of Polysorbate 80 and the different ratios of heptane
and ethyl acetate (EA) were tested. The results indicated strong intermolecular interactions between
Polysorbate 80 and benzyl acetate. It was concluded that increasing the amount of Polysorbate 80 led
to increased intermolecular interaction. Through the experimentation process, it was discovered that
Polysorbate 80 is a strong surfactant, retaining benzyl acetate in solution. Moreover, it was concluded
that the higher the ratio of heptane to ethyl acetate (EA), the lower Rf value of benzyl acetate.
| 14 |
Author(s):
Doah Kim.
Page No : 115-125
|
Susceptibility and Retention in Cults: A Systematic Review of Neurobiological, Psychological, and Group-social Influences
Abstract
Cults, also referred to as high-demand groups or new religious movements, are social formations
characterized by strong behavioral, informational, emotional, and cognitive control, often involving
manipulative practices and suppression of individual autonomy. Although the psychological
consequences of cult involvement are well documented, broader interdisciplinary explanations
remain limited. This systematic review applies Engel’s Biopsychosocial (BPS) framework to examine
cult membership as an interaction of neurobiological, psychological, and social factors. The review
aimed to identify key mechanisms associated with susceptibility to cult involvement and continued
membership. Of 97 screened records, 11 studies met the inclusion criteria. Findings identified ten major
biopsychosocial factors: neurobiological (n = 4), psychological (n = 3), and social/group-level factors (n
= 3). Neurobiological findings were largely derived from general social neuroscience and group behavior
studies rather than direct cult-population research, and should therefore be interpreted as associative
rather than causal. Across all three domains, social conformity and group cohesion consistently emerged
as central mechanisms underlying cultic influence. Overall, current evidence remains limited by small
samples, indirect measures, and the scarcity of longitudinal designs. Further empirical research is needed
to develop more individualized and evidence-based interventions for those affected by coercive groups.
| 15 |
Author(s):
Trisha Manipatruni.
Page No : 126-136
|
Nanotheranostics for Cancer: Integrating Imaging, Therapy, and Real-Time Monitoring
Abstract
Despite decades of progress, cancer is still frequently detected only at advanced stages, and
conventional treatments such as surgery, chemotherapy, and radiation therapy often lack tissue
specificity. These limitations have motivated the development of approaches that unify early detection
with targeted treatment in a single system. A nanotheranostic platform is an engineered nanoparticle
construct that integrates therapeutic and diagnostic functions within a single system. In preclinical
studies conducted both in vitro and in animal models, such platforms have shown early evidence of
the capacity to improve lesion visualization, enable image-guided treatment activation, and support
treatment-response monitoring. This review breaks down nanotheranostic platforms into three core
design dimensions: therapeutic modality (including photothermal therapy (PTT), photodynamic therapy
(PDT), chemotherapy, and multimodal systems), diagnostic readout (primarily fluorescence and magnetic
resonance imaging (MRI)) and targeting strategy (including passive accumulation via the enhanced
permeability and retention (EPR) effect, ligand-mediated active targeting, and organelle localization).
By integrating diagnostic and therapeutic components, these platforms are designed to increase tumorassociated
delivery and enable image-guided activation, with reported improvements in tumor control
in preclinical models. Across cited studies, multimodal nanotheranostic systems often demonstrate
improved tumor control relative to single-modality comparators within the same experimental model.
However, reductions in systemic toxicity and improvements in patient quality of life remain unverified in
clinical settings. To identify the most promising clinically translatable strategies, this review examines
critical barriers to translation, including immune clearance, batch-to-batch variability, manufacturing
scale-up, regulatory complexity, and long-term biodistribution. The review concludes by identifying
design principles associated with stronger translational positioning compared to platforms that remain
preclinical.
| 16 |
Author(s):
Yu Milan.
Page No : 137-142
|
Modeling the Detection of Cyber Intrusion with Network Traffic Features: A Regression-Based Study
Abstract
Cyber-attacks continue to impose serious threats to modern digital systems, creating an urgent need
for interpretable and effective methods in detecting intrusions. This study examines the use of network
traffic features to predict cyber intrusions by specifically applying a regression-based quantitative
framework. Using the publicly available NSL-KDD intrusion detection dataset, this study conducted
a quantitative analysis with a total of 125,973 records of network connections that were labeled as
either normal traffic or intrusion attempts. Connection duration, protocol type, number of failed login
attempts, server-side error rate, and connection count were the five chosen network-level features to
capture abnormal network behavior. A binary logistic regression model was employed to estimate the
likelihood for a given network connection to be classified as an intrusion. When evaluating the model
performance, accuracy, precision, recall and a confusion matrix were specifically utilized. The model
achieved an overall accuracy of approximately 90.8%, with high precision and recall for detecting
intrusion attempts. However, it shall be noted that simulated network traffic was used to derive NSLKDD
dataset as a benchmark dataset. Therefore, the results primarily indicate methodological feasibility
rather than deployment performance in the real-world setting. This indicated that the majority of attack
attempts were identified correctly, while minimizing false alarms. These findings support how the simple
mathematical models may be effective in detecting intrusions in the field of cyber-security and other
applications, while highlighting the importance of transparent and quantitative approaches to intrusion
detection. This study demonstrates that regression-based modeling may be effective in identifying cyber
threats as an interpretable framework in the use of network traffic data.
| 17 |
Author(s):
Sophie Engel .
Page No : 143-148
|
An Exploration into Panethnic Identity: A Bottom-Up Examination of Latin American Migration and Belonging in the United States
Abstract
This paper delves into the idea of a panethnic identity, and the specific Latin American migration
experiences that contradict and reinforce it. While Cristina Mora’s Making Hispanics argues the rise
of a unified “Hispanic” identity is mainly due to the intersection of lobbyists and advocacy attempts to
gain a wider base, this article challenges that top-down framework by exploring the nuanced experiences
of migrants from Mexico, El Salvador, the Dominican Republic, and Cuba. Through a more bottom-up
approach and analysis of migration routes, U.S. immigration policy, and sociopolitical treatment within
the US, the research reveals both the unifying and fragmenting influences on Hispanic identity. Shared
difficulties across countries like family separation and systemic exclusion strengthen panethnicity, yet
unique situations due to distance, legal treatment, and racial identity fracture the notion of a cohesive
Hispanic identity. I argue that panethnic identity functions less as a shared cultural reality than as
a contingent political framework—one that fractures under differentiated legal treatment and social
expulsion. Recognizing these fractures is essential for more accurate sociological analysis, equitable
policy design, and meaningful representation.
| 18 |
Author(s):
Colin Chien.
Page No : 149-158
|
Deafferentation and Network Dysregulation Hypotheses in Charles Bonnet Syndrome Mechanisms
Abstract
This systematic review evaluates the extent to which different hypotheses drive visual hallucinations
in Charles Bonnet Syndrome (CBS) patients. CBS is a rare condition characterized by complex, persistent
visual hallucinations (VH) in patients with normal cognitive function and vision impairment. At
present, the cause of CBS remains unclear; there is evidence that deafferentation, hallucinations caused
by a deprivation of visual stimuli, which propagates neural hyperactivity through cortical excitability,
is considered a putative mechanism by which CBS arises. Through Google Scholar and PubMed, 18
studies were included to support evidence. This emerging research posits that the condition stems
from changes in sensory and control neural networks. Studies have shown reorganization of functional
connectivity among different systems, including the default mode network (DMN), salience network
(SN), and visual network (VN) in CBS patients, modeling alterations in brain activity. Therefore, we
propose a multi-stage process which systematically combines the two hypotheses in order to clarify the
underlying mechanisms behind CBS. High-order network dysregulation causes gating failures, leading
hallucinations from visual deafferentation to be conceptualized.
| 19 |
Author(s):
Fangzhou Zhai.
Page No : 159-168
|
Responding to Capitalism: Consumerism, Identity, and Environment in Spirited Away
Abstract
Released during Japan’s prolonged post-bubble economic stagnation, Spirited Away (2001) reflects
widespread social anxieties surrounding consumerism, labor uncertainty, environmental degradation,
and the erosion of traditional values in late-capitalist Japan. This paper examines Spirited Away as
a cinematic critique of capitalist modernity in Japan, focusing on consumerism, identity erosion,
hierarchical labor, and environmental pollution. Drawing on Adrian Ivakhiv’s Ecologies of the Moving
Image, the analysis applies the anthropomorphic, geomorphic, and animamorphic dimensions to explore
how cinematic spaces, characters, and spiritual beings within the film actively produce meaning rather
than merely telling a story. Situating the film within Japan’s postwar economic growth, bubble-era
excess, and Shinto belief, the paper argues that Miyazaki portrays capitalism as an ecological system that
reshapes human behaviour, undermines spiritual values, and disrupts the balance between humans and
the environment. Through close analysis of key figures including Chihiro, Yubaba, No-Face, bathhouse
workers, and the polluted river spirit, the paper demonstrates how consumerism dehumanizes subjects,
hierarchy normalizes exploitation, and environmental degradation emerges as a lived, embodied
consequence of industrial development. Ultimately, Spirited Away proposes an alternative ethical vision
grounded in Shinto principles of purification, relational identity, restraint, and collective responsibility,
offering a critique of capitalism that is moral, ecological, and cultural instead of purely economic.
| 20 |
Author(s):
Wenhan Zhang.
Page No : 169-178
|
Glucose Metabolism and Antioxidant Alterations in Glioma Stem Cells Facilitating Glioblastoma Recurrence
Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Its poor prognosis
largely arises from its tendency to recur despite standard treatment with surgery, radiotherapy, and
temozolomide. Glioblastoma stem cells (GSCs) are a major driver of recurrence because they possess
stem-like properties and can regenerate the tumor after resisting treatment. Increasing evidence indicates
that metabolic reprogramming within GSCs supports their survival following therapy. GSCs can switch
between glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) in response to therapyinduced
stress. Unlike bulk GBM cells, which predominantly rely on aerobic glycolysis known as the
Warburg effect, GSCs can be glycolytic, OXPHOS-dependent, or hybrid, and can transition between these
states under stress. Furthermore, the ability of GSCs to survive and recur also depends on antioxidant
pathways that counteract therapy-induced oxidative stress. Key mechanisms include activation of the
NRF2 pathway and upregulation of the glutathione and thioredoxin systems. This review synthesizes
current literature to highlight how alterations in glucose metabolism and antioxidant reprogramming in
GSCs contribute to GBM recurrence. It also discusses key therapeutic challenges, including intratumoral
heterogeneity, metabolic state switching, limited drug delivery across the blood-brain barrier, and the
risk of toxicity when disrupting metabolic pathways required by normal neural cells.
| 21 |
Author(s):
Isabella Dolan.
Page No : 179-191
|
Governance Design and Implementation in Textile Extended Producer Responsibility: A Comparative Analysis
Abstract
Extended Producer Responsibility (EPR) laws have been implemented in several countries as a policy
response to the rapid growth of textile consumption and waste, yet textile EPR systems vary widely
in design, governance, and transparency. This paper develops a structured comparative framework to
examine how differences in policy structure influence implementation quality and reported outcomes
across international textile EPR systems. While much of existing literature focuses on general EPR
principles or country-specific case studies, this study provides a systematic cross-country comparison
of textile EPR governance and performance. Using an author-created comparative policy analysis, this
study develops an Implementation Quality Index (IQI) and an Outcomes Index (OI) to evaluate textile
EPR systems in France, the Netherlands, Sweden, Hungary, and Australia. This dual-index framework
separates governance design and reported policy outcomes, recognizing that many textile EPR systems
are either too recent or insufficiently transparent to support a reliable comparison based solely on
performance data. The analysis focuses on five key design patterns: governance centralization, product
scope breadth, eco-modulation strength, enforcement clarity, and Producer Responsibility Organization
(PRO) transparency. Outcome indicators are assessed where sufficient public data is available. By
evaluating implementation design and reported outcomes separately, the IQI and IO demonstrate that
stronger results are associated with centralized governance, broad product scope, differentiated fee
structures, clear enforcement mechanisms, and transparent reporting practices, which offers practical
insights for policymakers designing or reforming textile EPR systems.
| 22 |
Author(s):
Salbee Hovanes .
Page No : 192-198
|
Comparative Effects of Cold and Heat Therapy on ACL Rehabilitation Among Teen Student-Athletes in the Western United States
Abstract
Student-athletes most frequently experience anterior cruciate ligament (ACL) injuries during sports
that require sudden stopping, pivoting, jumping, or rapid changes in direction. Rehabilitation is a critical
component of recovery and return to sport, with cold therapy (also known as cryotherapy) and heat
therapy commonly used for symptom management. This study examines how teenage student-athletes
in the Western United States use cold and heat therapy following an ACL injury and how they perceive
the effectiveness of each modality. A mixed-methods approach was used, combining a structured review
of peer-reviewed literature with survey-based data on rehabilitation experiences, therapy usage, and
perceived outcomes. Findings indicate that cold therapy is more frequently utilized and preferred for
pain and swelling management, particularly during early rehabilitation stages, whereas heat therapy is
primarily used to promote flexibility and muscle relaxation during later phases of recovery. Overall, the
results suggest that effective ACL rehabilitation should follow a stage-specific approach, with treatment
selection tailored to the injury phase and individual recovery goals. These findings may help inform
athletes, clinicians, and caregivers in developing more individualized rehabilitation strategies. The study
was conducted in accordance with Glendale Unified School District (GUSD) guidance, and because
it involved a minimum-risk, anonymous survey, institutional Review Board (IRB) approval was not
required.
| 23 |
Author(s):
Krrish Singh Sardar.
Page No : 199-205
|
Direct Anterior Versus Posterior Approach in Total Hip Arthroplasty: A Narrative Evidence Review of Recovery, Safety and Cost
Abstract
In the 2000s, new advances in imaging technologies and specialized surgical tables revitalized a
150-year-old method for total hip arthroplasty: the direct anterior approach. This newfound surge in
usage of the direct anterior approach challenged the use of the more practiced posterior approach. In
fact, studies show that many clinics and surgeons’ websites state that the direct anterior approach is
better for its muscle-sparing qualities and shorter recovery times. However, only a small percentage
of these sites list the possible risks or cite peer-reviewed journals as evidence for their claims. This
paper takes an evidence-based perspective using key factors such as the recovery process, recent use of
inflammatory biomarkers, safety, and aggregated cost to evaluate whether the direct anterior approach
truly is as effective and safe as these surgeons and clinics claim. Ultimately, the general consensus from
current medical literature supports the posterior approach to total hip arthroplasty via consideration of
the most important factors.
| 24 |
Author(s):
Kenneth Yu.
Page No : 206-214
|
Artificial Intelligence and Machine Learning Approaches in Seizure Forecasting and Detection: A Comprehensive Review
Abstract
A seizure is a sudden, uncontrolled surge of electrical activity in the brain that affects millions of
people leading to serious impairments. Seizures can be caused by many factors commonly attributed
to epilepsy; however, other factors, such as brain tumors and drugs, can induce seizures as well. Such
seizures, especially in early childhood, can have a devastating impact on a child’s development, or
induce physical harm due to the immediate loss of consciousness and other severe side effects. Currently,
seizure treatments are limited, with medications and surgery being the two main points. However,
models based on artificial intelligence and machine learning have emerged as newer prevention methods
for seizure treatments, allowing for forecasting ahead of time to apply treatments in case a seizure can
happen. This systematic review paper investigates recent literature, from 2022 to 2025, and explores
the application of artificial intelligence and machine learning to seizure forecasting and detection. The
sources are screened through the PRISMA (Preferred Reporting Items for Systematic Reviews and
Meta-Analyses) flowchart. Additionally, the review addresses the limitations, reliability, accuracy, and
barriers to widespread use indicating that seizure care is shifting from reaction to prediction. Through
artificial intelligence and machine learning, seizures can now be detected before they occur, allowing
earlier intervention and improved patient safety. This review establishes the state of the art in models,
performance, and challenges, and will help researchers continue to advance in the realm of seizure
forecasting, thereby improving the quality of life of many people around the globe.
| 25 |
Author(s):
Rithvik Nasika.
Page No : 215-222
|
Therapeutic Cancer Vaccines: Mechanisms, Clinical Evidence and Translational Challenges
Abstract
Cancer has persisted as one of the major causative agents of death around the world, fueled by factors
associated with the uncontrolled growth of mutated cells that are normally difficult to recognize by the
body’s immune system or to combat using conventional therapeutic approaches that include chemotherapy,
irradiation, and surgery. This paper reviews various cancer vaccines, which have emerged as among
the most promising immunotherapies. Cancer vaccines induce anticancer responses by activating the
body’s adaptive immune system to recognize and destroy cancer cells. Fundamentally, cancer vaccines
fall into several categories, including preventive, cell-based, nucleic acid-based, peptide/protein-based,
and oncolytic viral vaccines. Currently, several clinical studies indicate that several forms of cancer
vaccines offer effective promise in their ability to induce effective T cell responses in cancer patients,
although these vaccines remain far from having achieved widespread clinical implementation due to
several underlying challenges associated with cancer vaccines, including immunosuppressive tumor
microenvironments, antigens’ immunogenicity, and the high production and engineering complexity
of personalized vaccines. Nonetheless, cancer vaccines show unequivocal promise as one of the most
effective approaches in cancer therapy in the near future.
| 26 |
Author(s):
Patrick Zou.
Page No : 223-229
|
Bound Niacin Formation and Biofortification of Free Niacin in Maize
Abstract
Maize (Zea mays) is one of the major staple crops worldwide and plays a crucial role in the human diet
as a source of vitamin B3, also known as niacin. However, a large proportion of niacin exists in a bound
form that is poorly bioavailable to humans. When a population depends on bound niacin, insufficient
niacin availability will lead to pellagra, a disease caused by niacin deficiency. Due to the significant
negative impact of pellagra, researchers have examined the biochemical mechanism of bound niacin
information. The review summarizes key genes and environmental factors that affect the total niacin
and bound niacin concentrations in the maize kernel. Understanding the formation mechanism of bound
niacin can inspire scientists to develop possible methods to enhance the bioavailability of niacin and
nutritional biofortification, thereby reducing the global issue of niacin-deficiency-induced hidden hunger.
| 27 |
Author(s):
Rehan Shaikh.
Page No : 230-237
|
Engineering Adaptation to Climate-Driven Structural Hazards: A Comparative Review of Fire, Earthquake, and Tornado Resilience
Abstract
Climate change has intensified the frequency and severity of natural hazards, requiring structural
engineers to develop more resilient strategies against fire, earthquake, and tornado events. This literature
review examines how structural engineering is adapting to climate-driven hazards through advances
in materials, Artificial Intelligence (AI) modeling, and design. Advances in materials, including shapememory
alloys and high-performance fiber-reinforced concrete, improve durability and post-event
recoverability, while performance-based and interdisciplinary design approaches provide more efficient
multi-hazard resilience. Despite this progress, widespread implementation is limited by high costs,
inconsistent global codes, retrofitting challenges, and resource constraints in developing countries. This
review underscores the need for global collaboration, unified frameworks, and expanded investment
to ensure equitable access to hazard-resistant technologies. Overall, the findings show that structural
engineering is rapidly evolving toward more sustainable, data-driven, and adaptable solutions capable of
addressing climate-driven risk.
| 28 |
Author(s):
Matthew Kim.
Page No : 238-244
|
A Study of Paraguayan Athletes’ Instagram Posts: What They Can Tell Us about the Effects of Social Media on Spanish-English Code-Switching in South America
Abstract
This research examines how Instagram influences the integration of English into everyday digital
communication among Paraguayan athletes and their fans. Through a qualitative analysis of a small
sample of 20 Instagram posts from professional Paraguayan athletes, the study identifies frequent
instances of code-switching and English word use within primarily Spanish-language contexts.
Drawing on linguistic and sociocultural frameworks, the research highlights how athletes sometimes
employ English strategically to express identity, emotion, and global affiliation. The findings reveal
that Instagram may function as a platform where English expressions circulate among athletes and
their followers, as fans imitate athletes’ language use, blending English and Spanish in their own posts.
Rather than making claims about national linguistic trends, the findings provide an exploratory snapshot
of language use within a specific digital sports community. This process reflects broader patterns of
globalization, digital communication, and linguistic adaptation, showing that social media has become
a driving force in reshaping language practices and promoting hybrid forms such as Spanglish within
Paraguay’s sports communities.
| 29 |
Author(s):
Sophia Pientka.
Page No : 245-255
|
Youth Financial Access and the Conditions of Economic Agency: A Cross-National Qualitative Analysis of Youth Perspectives
Abstract
Youth financial access is increasingly recognized as a contributor to economic participation and longterm
development, yet youth perspectives remain largely absent from global financial inclusion research,
which often relies on technical indicators such as account ownership or product usage. This study
examines how youth aged 16 to 21 across twenty-four countries define financial freedom and describe
the social, cultural, and institutional conditions shaping their economic agency. Using qualitative openresponse
survey data from 60 participants, responses were manually coded into eight thematic categories
capturing interconnected dimensions of youth financial access, including definitions of financial
freedom, barriers to financial independence, cultural and family expectations, financial literacy and
education, perceived opportunities, policy and systemic support, and proposed reforms. Across regions,
youth consistently framed financial access as a foundation for autonomy and decision-making rather
than as a purely technical condition. Regional contrasts emerged in the sources of constraint, with
respondents from South Asia and Sub-Saharan Africa emphasizing structural barriers such as labormarket
instability, limited institutional support, and family financial obligations, while respondents from
North America and Western Europe more frequently cited gaps in financial literacy and uncertainty
about opportunity pathways. Across all regions, financial agency was described as socially embedded,
with autonomy often negotiated through family expectations rather than exercised independently. These
findings suggest that prevailing financial inclusion metrics may overlook dimensions of access central
to youth lived experience and underscore the value of incorporating youth-defined perspectives into
research and policy frameworks concerned with economic participation.
| 30 |
Author(s):
Pracheth Vitthaladevuni.
Page No : 256-265
|
The Impact of Antibiotics on the Human Gut Microbiome
Abstract
This paper analyzes the adverse effects of antibiotics on the human gut microbiome, combining
survey data with published literature. A 31-question survey was administered across multiple U.S.
states (CA, WA, MA, NC, TX, IL, GA, NJ, RI, AZ), Edmonton and Toronto in Canada, and Hyderabad,
Kolkata, Bengaluru, and Surat in India, and Basel in Switzerland, to collect public experiences with
antibiotic use. Respondents (n=103) were surveyed and the results were compared with peer-reviewed
studies and national databases, including the CDC and NIH. The findings suggest that antibiotic use
disrupts gut microbial diversity, leading to digestive issues, immune dysregulation, skin irritation, and
mood disorders. This disruption is especially concerning given the essential role of the gut microbiome
in digestion, immunity, and the gut–brain axis. Literature demonstrates that microbiome development
begins at birth and stabilizes in early childhood, but antibiotics can permanently reduce bacterial
diversity. We discuss how antibiotic-induced dysbiosis contributes to chronic conditions, explore the
relationship between the gut microbiome and immunity, and examine alternatives to antibiotics such as
probiotics, prebiotics, bacteriophage therapy, and fecal microbiota transplantation. Understanding both
the risks of antibiotics and pathways for microbiome restoration is crucial for future health management.
| 31 |
Author(s):
Felix Kong.
Page No : 266-277
|
A Comparison of Emotional and Behavioral Outcomes in Group- and Individual-Based Physical Activity Interventions of Youth with Autism Spectrum Disorder
Abstract
Youth with Autism Spectrum Disorder (ASD) tend to be more socially avoidant and emotionally
dysregulated. Combined with their more sedentary lifestyle, these factors contribute to their higher rates
of loneliness and lower quality of life. Physical activity intervention (PAI) is increasingly used to address
these deficits. However, research comparing characteristics of PAI, such as group versus individual
setting, remains limited. The review investigates and compares the behavioral and emotional outcomes
of individual- and group-based PAI for youth with ASD. A systematic review across PubMed and
ScienceDirect was used to identify relevant studies. Following PRISMA guidelines, 326 studies were
assessed for potential eligibility, and 11 studies written in English from 2005 to 2025 were extracted to be
analyzed. Findings show that group- PAI consistently improved pattern-based behaviors, mood stability
and reactivity, while individual PAI more consistently improved arousal control behaviors. Group PAI
was found to more consistently improve overall behavioral and emotional outcomes than individual PAI
does. Furthermore, longer duration and higher complexity were associated with group PAI, which could
explain these findings. Future research can investigate the independent influences of these specific PAI
characteristics on outcomes to pinpoint how they can be optimized in a PAI setting.
| 32 |
Author(s):
Aadith P. Maganti.
Page No : 278-288
|
The Neuroprotective Nexus: GLP-1 Receptor Agonists, Sleep, and the Glymphatic System in Alzheimer’s Disease Prevention
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the deposition
of extracellular amyloid-beta (Aβ) plaques and intracellular tau protein neurofibrillary tangles (NFTs),
accompanied by prominent neuroinflammatory responses. Treating AD is extremely challenging because
of its complex etiopathogenesis. AD and type 2 diabetes mellitus (T2D), a chronic metabolic disorder,
share common pathophysiological features, such as insulin resistance and sleep and circadian rhythm
disruptions, all of which are well-recognized risk factors for AD. Recently, glucagon-like peptide-1
receptor agonists (GLP-1RAs), approved medications for T2D and obesity, have been investigated as
candidate disease-modifying agents for AD because of their neuroprotective, anti-inflammatory,
improved insulin signaling, and anti-amyloidogenic properties. However, the mechanisms by which
GLP-1 signaling affects sleep-wake regulation remain poorly defined. Thus, this review synthesizes
the evidence linking GLP-1RAs to sleep architecture, specifically to sleep spindles and non-rapid
eye movement (NREM) of the sleep-wake cycle, and to aquaporin 4 (AQP4)-dependent glymphatic
clearance pathways, focusing on how these mechanisms could be leveraged to address sleep dysfunction
and impaired clearance in AD. GLP-1RAs-driven mechanisms restoring the function of the glymphatic
system, as well as the similar treatment benefits of melatonin, the key hormone regulating the sleepwake
cycle and circadian rhythm, are discussed. In addition, the rationale for combination strategies
(GLP-1RAs plus melatonin) to target complementary sleep and glymphatic clearance is highlighted,
while emphasizing the need for prospective clinical testing.
| 33 |
Author(s):
Emily Cheong.
Page No : 289-295
|
Modeling Efficiency and Equity in Disability-Based Accessibility Resource Allocation Using Linear Programming
Abstract
Communication accessibility is essential for the social participation of people with disabilities.
However, limited public funding makes it difficult to prioritize accessibility investments. This study
proposes a quantitative framework to examine how resources may be allocated for communication
accessibility under budget constraints. Utilizing population-level disability prevalence data from the
American Community Survey, inclusion weights were generated for four domains of accessibility:
hearing, vision, cognitive, and independent living. A linear programming model was developed to
maximize an overall communication inclusion score based on these weights. With a baseline efficiencydriven
formulation, the model produced a corner solution, directing all resources to a single domain
with the highest inclusion weight, exhibiting the limitations of efficiency-only decision-making for
accessibility policy. To address this limitation, the model was extended with constraints related to equity,
including minimum baseline allocations and proportionality conditions across domains. The revised
model produced a more balanced allocation of investments while decreasing the exclusion of smaller
disability groups. The findings in this study indicate how decisions for resource allocation may be
formalized with mathematical optimization while revealing the limitations of efficiency-only allocation.
This framework provides a transparent tool for supporting the decision in allocation in the perspective of
planning accessibility under constrained resources.
| 34 |
Author(s):
Aarush Arun.
Page No : 296-306
|
Quantifying Financial Disparities in Corporate Sustainability: Predictive Modeling using Support Vector Machines and Regression Analysis
Abstract
As the world becomes increasingly aware of climate change and recognizes the need for
environmental, social, and governance (ESG) consciousness, it has become necessary to balance
economic and private sector growth with ecological sustainability. Hence, many efforts have been made
to categorize businesses by their sustainability performance, aiming to incentivize sustainable corporate
practices. Yet, despite this, it is actively debated whether being a sustainable company is beneficial to
companies in terms of profit and economic growth. This paper examines the structural differences and
associations between being a corporate sustainability leader and key financial metrics, including stock
performance, profit, and growth. This study utilizes the Corporate Knights Global 100, a ranking of the
world’s 100 most sustainable public companies with revenue exceeding 1 billion USD. By employing
rigorous nonparametric statistical testing, subsequent False Discovery Rate corrections, and predictive
modeling using Support Vector Machines, this study compares these highly ranked firms against their
unranked peers. Despite potentially strengthening a company’s brand image by striving to be at the
forefront of corporate sustainability, findings suggest that ranked companies are generally associated
with inferior financial performance compared to their unranked peers, raising questions about the
potential trade-offs of ESG leadership.
| 35 |
Author(s):
Suhui Kim.
Page No : 307-312
|
Geographic Variation in Autism Spectrum Disorder Prevalence Across CDC ADDM Surveillance Sites with Socioeconomic Indicators in the United States
Abstract
This study examined national and regional trends in autism spectrum disorder (ASD) prevalence in
the United States. Publicly available data from the Centers for Disease Control and Prevention (CDC)
Autism and Developmental Disabilities Monitoring (ADDM) Network were used for the data analysis.
A population-level descriptive surveillance analysis was conducted using site-year observations of
reported ASD prevalence per 1,000 8-year-old children from the early 2000s through the early 2020s. To
summarize overall prevalence patterns, descriptive statistics were used. In addition, a linear time-trend
model was generated and applied to quantify temporal change across reporting cycles. The analysis
demonstrated a clear and sustained increase in reported ASD prevalence over time, with significant
geographic variation across surveillance areas. There was a right-skewed distribution of prevalence
estimates, indicating that a smaller number of regions showed significantly higher reported rates, while
many regions reported moderate prevalence levels. In the time-trend model, ASD prevalence rose
substantially across reporting years, supporting the interpretation that the increase in reported diagnoses
reflects a long-term structural trend rather than isolated fluctuations. Geographic differences were
interpreted with reference to prior studies suggesting that socioeconomic conditions, healthcare access,
diagnostic capacity, and public awareness may contribute to variation in identification patterns across
regions. Since the reproducible quantitative analysis was based on ADDM prevalence observations
rather than a fully merged socioeconomic dataset, the findings in this study and their interpretations
should be understood as ecological and descriptive rather than causal. Overall, the results emphasize
the importance of considering structural influences when interpreting ASD prevalence trends and
geographic disparities.
| 36 |
Author(s):
Yash Jaju, Divya Ramamoorthy.
Page No : 313-318
|
Structure-Based Drug Discovery of New Compounds Targeting Exon 1 N-terminus Region of Mutant Huntingtin
Abstract
Affecting hundreds of thousands of people around the world, Huntington’s Disease is a
neurodegenerative disorder causing involuntary movements, poor-decision making, nervous system
shutdown, and ultimately death. The aim of this project was to identify molecules as inhibitors to limit
the disease-causing mutation, known as mHTT (mutant huntingtin). We used the PDB 4FEB in this study.
Three groups of compounds, 50 CNS (central nervous system) compounds, 13 mHTT inhibitors, and 50
peptidomimetics that we screened in the study. In addition, we analyzed the binding site and created a
docking grid to prepare the mHTT protein for SP (standard precision) and XP (extra precision) docking.
We also performed QikProp analysis, which gave us the log BB, molMW, and Percent Human Oral
Absorption of the ligand. Finally, we created ligand interaction diagrams between the top-performing
ligands and the 4FEB chain to demonstrate the key interactions. Top-scoring ligands were selected based
on their docking scores, with acceptable scores ranging from -3 to -4.5 after XP Docking. While the
two top-scoring compounds showed strong computational binding efficiency, in vitro testing is required
to ensure the compounds execute effectively as they did computationally. Going forward, our goal is to
continue researching and computationally docking ligands that can drive the design of new compounds
for effectively targeting the mHTT protein in silico.
| 37 |
Author(s):
Jaydn Su .
Page No : 319-326
|
Inputting Missing Values in Mechanical Materials Data: Accuracy and Statistical Effect of Mean, Median, and KNN Methods
Abstract
This study explores how different methods for handling missing data change the accuracy of
mechanical property datasets. A dataset containing ultimate tensile strength (Su) and related mechanical
properties was used, with 100 Su values randomly removed to simulate realistic data loss. Three
commonly used methods were tested: mean substitution, median substitution, and k-nearest neighbor
(KNN) imputation. Each completed dataset was then compared to the original to see how well statistical
relationships were maintained. The results indicated that the median imputation method produced the
most accurate reconstruction in this dataset and under MCAR simulation among the tested methods,
maintaining an almost exact correlation with the original Su values with a Pearson correlation coefficient
(r) value of 0.9987 and a coefficient of determination (R²) value of 0.9487 in the linear regression model.
Both mean and KNN imputation performed sufficiently, but introduced larger deviations from the
original relationships. Overall, the findings show that under the MCAR missingness simulation used here
and within this specific mechanical-property dataset, the median imputation method provides the most
effective balance between accuracy and preservation of statistical structure among the three methods
tested, suggesting that median imputation may serve as a practical solution for researchers and engineers
who regularly work with incomplete mechanical property data.
| 38 |
Author(s):
Xinlei Gao.
Page No : 327-335
|
How do Different Psychological, Social, and Cognitive Factors Contribute to the Rise in Mental Distress Among Adolescents?
Abstract
Adolescent mental health issues have risen sharply over the past decade, emerging recently as a
global health concern. This literature review looks into how different factors such as psychological,
social and cognitive factors have contributed to the increasing prevalence of mental distress among youth
with emphasis on the impacts of the COVID-19 pandemic. Evidence has indicated that several factors
interconnectedly exacerbate mental distress, including heightened academic pressure, increased social
media use, bullying, dysfunctional family, dynamics, and substance use. COVID-19 has appeared as
a consistent catalyst that aggravates preexisting vulnerabilities. Collectively, these findings underscore
the need for prevention and intervention strategies to address escalating mental health challenges facing
adolescents today.
| 39 |
Author(s):
Edward W Zhu.
Page No : 336-343
|
Impact of Music Emotional Characteristics on Heart Rate in a Community Sample
Abstract
Elevated heart rate is associated with an increased risk of cardiovascular mortality. Although prior
research suggests that music has an impact on heart rate, the differential effect of music has not been
well understood. This study assessed the effect of music with varying emotional characteristics on heart
rate, and evaluated how arousal (intensity) and valence (pleasantness), the two aspects of emotional
characteristics, influenced heart rate. Five participants were recruited to this quasi-experimental study.
Each participant’s heart rate was continuously monitored with an Apple Watch while they listened to
music representing five distinct emotional characteristics, along with a no-music control condition.
. Linear mixed models was used to estimate the effects of each music on heart rate, accounting for
the clustering of repeated heart rate measures within each participant. Compared with the control
session, stormy and romantic pieces significantly increased heart rate while dreamy and nostalgic pieces
significantly decreased heart rate. Further analyses suggested that high arousal was positively associated
with and pleasantness was negatively associated with heart rate; no significant interaction between
arousal and valence was observed. These findings suggest that specific emotional characteristics of
music differentially influence heart rate and may have clinical implications for the use of music in stress
reduction, rehabilitation, and exercise settings.
| 40 |
Author(s):
Tanya Dandapani.
Page No : 344-354
|
When Knowledge Isn’t Enough: Financial Literacy, Social Media Advertising, and Impulse Buying in Adolescents and Emerging Adults
Abstract
Social media now plays a significant role in shaping how adolescents and young adults discover and
buy products, often encouraging impulse purchases. At the same time, schools across the United States
are placing greater emphasis on financial literacy to help students make responsible spending decisions.
This study investigates the relationship between financial literacy, financial confidence, and impulse
buying behavior among adolescents and emerging adults, with a specific focus on the influence of social
media advertising. This research uses an anonymous online survey completed by 76 participants aged
13 to 24. The survey measured objective financial literacy, self-reported financial confidence, exposure
to social media advertisements, and self-reported impulse-buying behavior using both self-reported
questions and hypothetical purchasing scenarios. The results show that although financial literacy and
financial confidence increase with age, impulse buying and purchases influenced by social media also
increase, especially among participants aged 18–24. Higher financial literacy did not lead to lower
impulse buying, and financial confidence did not consistently predict responsible spending behavior.
These findings suggest that financial knowledge alone may not be enough to reduce impulsive spending
in digital environments.
| 41 |
Author(s):
Yash Nair.
Page No : 355-363
|
The Relationship Between Informal Employment, Tax Revenue, and Economic Growth: A Cross-Country Analysis
Abstract
This paper examines the relationship between tax collection, GDP growth, and the informal economy,
addressing the question of how limiting informal activity and maximising tax collection can boost
GDP growth in a country. The paper first reviews existing literature to point out established economic
theories and recognise real-world examples that link informal activity, GDP growth and the efficiency
of tax collection. Secondly, a quantitative analysis was performed using the variables of average tax
revenue as a percentage of GDP, mean annual GDP per cent growth and average informal employment
as a percentage of total employment. This relationship between variables is examined using a variate
regression model and a correlation matrix, which aid in examining the exact measure of the relationship
among the chosen variables. The results from the data analysis present a strong positive relationship
between average tax revenue as a percentage of GDP and mean annual GDP per cent growth, additionally
presenting a strong negative correlation between average tax revenue as a percentage of GDP and
average informal employment as a percentage of total employment. These findings suggest that weak
and inefficient tax collection can discourage formal economic participation and limit long-term growth.
Based on the findings, policy changes that make the tax system simpler, proper and strong enforcement
mechanisms and administrative capacity building are all recommended to potentially enable economic
growth, maximise tax revenue while simultaneously suppressing the reliance on informal activity. This
paper acknowledges limitations in the consistency and reliability of the data on informal activity, which
may affect the robustness of the data.
| 42 |
Author(s):
Jiaming Qian.
Page No : 364-373
|
Comparative Analysis of DeepFace Attribute Classification on the FairFace Validation Set
Abstract
Automated facial analysis systems increasingly estimate demographic attributes such as age, gender,
and race from images, yet performance disparities across demographic groups remain a substantial
concern. This study evaluates the off-the-shelf DeepFace attribute classifier, accessed through the
deepface Python library, on the FairFace validation set. FairFace labels are treated as ground truth, and
DeepFace is applied without additional fine-tuning. To align the label spaces, FairFace’s East Asian
and Southeast Asian categories are merged into a single Asian class to match DeepFace’s race output.
Performance is assessed using age-range accuracy, mean signed age error (age bias), overall accuracy,
balanced accuracy, per-class precision, recall, and F1 score (F1), macro-averaged F1, Cohen’s kappa
coefficient (κ), and chi-square tests of race-related error disparities. On 10,954 validation images,
DeepFace achieved age-range accuracy of 0.28437 with a positive age bias of 3.65926 years, gender
accuracy of 0.72074, and race accuracy of 0.59540. Gender results showed strong asymmetry between
female recall (0.44537) and male recall (0.96616), while race results showed substantially lower F1 scores
for Indian, Latino/Hispanic, and Middle Eastern groups than for Asian and Black groups. These findings
show that strong face-verification performance does not necessarily translate into equitable demographic
attribute prediction and underscore the need for subgroup-level evaluation and fairness-aware model
development.
| 43 |
Author(s):
Anya C. Karyekar .
Page No : 374-382
|
Cancer Diagnosis and Earnings: Evidence on Labor-Force Exit and Sex Differences in the United States
Abstract
Serious health shocks such as cancer have the potential to significantly disrupt labor market outcomes.
However, the economic consequences of a cancer diagnosis, particularly across sexes and baseline
employment status, remain incompletely understood. This study utilizes a large US household survey,
the Panel Study of Income Dynamics (PSID), to analyze how cancer affects labor market outcomes using
a longitudinal event-study design, by examining changes in earnings before and after diagnosis. We
analyze earnings trajectories before and after cancer diagnosis to identify causal effects of health shocks
on economic outcomes. The results demonstrate parallel trends, indicating no change in the pre-diagnosis
period across groups and a substantial and statistically significant decline in earnings following cancer
diagnosis, with effects that persist and deepen over time, reaching up to 70% reductions in the postdiagnosis
period. Comparative analyses by sex indicate that while women exhibit somewhat smaller
declines than men, these differences are not statistically significant. Average earnings change only
minimally for the subgroup of individuals who earn at least the statutory minimum wage throughout the
observation period, suggesting that the primary mechanism driving overall earnings losses is labor-force
exit rather than wage reductions among continuously employed workers. The identifying assumptions
of the event‑study design are supported by the absence of differential earnings trends prior to diagnosis.
These results underscore the importance of policies aimed at preserving labor market attachment for
individuals facing serious health shocks such as cancer patients, including flexible work arrangements,
comprehensive medical leave policies, or workplace accommodations designed to mitigate the economic
burden of cancer.
| 44 |
Author(s):
Raphael Sutiono.
Page No : 383-399
|
The Interaction of Population Density and Flood Infrastructure in Urban Flood Risk Areas: A Narrative Review of Economic Loss and Social Vulnerability
Abstract
Flooding is one of the most catastrophic natural disasters impacting millions of people worldwide
and causing trillions of dollars in annual economic loss. While literature exists on the subject, it often
focuses on a limited scope of variables at a time, rather than synthesizing a holistic perspective of
factors contributing to flood severity and their consequences. This narrative review synthesizes literature
from various contexts to better evaluate how population density and the extent of protective flood
infrastructure are related to economic losses due to flooding. The discussion and analysis points include
a narrative review and analysis of literature that measures loss assessments, community infrastructure
characteristics, urbanization trends, and adaptations within cities across the globe. Across the reviewed
studies, a recurring pattern indicates that damage from flooding escalates with population density, but
they are significantly reduced where all-encompassing protection infrastructure is present; the addition
of green infrastructure and proactive adaptation measures - especially those guided by effective policy -
further enhances resilience to flooding. Taken together, the literature consistently highlights population
exposure and infrastructure capacity as leading determinants of loss outcomes. This review underscores
implications for urban planning, policymaking, and disaster mitigation by emphasizing the need not only
to evaluate hazard exposure, but also to adapt the physical urban environment - through infrastructure
upgrades and equitable planning - for greater sustainability and resilience. The approach presented in this
narrative review synthesizes existing literature to point to opportunities to better combine infrastructure,
demographic, and spatial data for enhanced disaster preparedness.
| 45 |
Author(s):
Leo Ahtaridis.
Page No : 400-405
|
Cross-National Differences in the Pedagogy of Fundamental Algebra: A Comparative Review of Textbooks from Three Countries
Abstract
This study examines differences in the textbook teaching of basic algebra in three different countries.
Primary sources included nationally published curricula and popular algebra textbooks from the United
States, United Kingdom, and India. To facilitate comparison, the chapters reviewing equations with
parenthetical expressions were selected for analysis. Common to all textbook examples were the age of
the intended audience and the introduction of three principles: the distributive property, simplification
of algebraic expression by combining like terms, and reduction by the greatest common factor. The
curricula differed, however, in the order in which the topics were presented, and this corresponded to
differences in the approach to problem sets, namely in the order of operations recommended. Further
study is needed to determine if these observed variations in textbook approach become more pronounced
in advanced algebraic curricula, and whether this leads to any appreciable inter-country differences in
problem-solving methodology or mathematical reasoning between student populations.
| 46 |
Author(s):
Jane Hong, Francis Hong, Jaehee Kim, Michelle Tan, Brooke Yu, Rebecca Tang.
Page No : 406-412
|
An In-Vitro Disk Diffusion Comparison Study of Chlorhexidine Gluconate and Salt: Application of Salt in Oral Treatments
Abstract
Oral infection is a prevalent condition that affects billions of individuals globally. This leads countries
to incur significant expenses to treat oral infections. One of the most commonly used antiseptics,
chlorhexidine gluconate, is a costly treatment. To assess this situation, we focused on finding a proxy
for chlorhexidine gluconate under oral application. We hypothesized that a certain salt concentration
would have a similar level of antiseptic effects as chlorhexidine gluconate. To compare the antiseptic
properties of the chlorhexidine gluconate to different salt concentrations, we first collected the bacteria
from our teeth. Then, we grew the bacterial cultures in an incubator. After bacterial colonies formed, we
collected them and mixed them in water to make a bacterial solution. Then, we suspended the solution
in another Petri dish and placed sterile disks that had been soaked in chlorhexidine gluconate and
salt solutions. We observed the diameter of the zone of inhibition of each sterile disk as a measure of
antiseptic effectiveness. Results showed that a 10% salt concentration solution produced similar effects
to chlorhexidine gluconate. This result suggests that salt can be a potential alternative to chlorhexidine
gluconate, as it is cost-effective, naturally occurring, and capable of producing similar effects.
| 47 |
Author(s):
Sharanya Singh.
Page No : 413-419
|
Advancing Alzheimer’s Disease Research Using Brain Organoids and Organ-on-Chip Technologies
Abstract
Alzheimer’s disease ranks as one of the gravest health threats facing humanity today. This disease
progressively impairs memory, cognition, and neuronal integrity in millions worldwide and inflicts severe
personal and social loss. Its profound impact, notably, has elevated it to a top priority in biomedical
research. However, established models, including animal studies and two-dimensional cell cultures,
often fall short and are unable to replicate the human brain’s complexity in both structure and function.
Consequently, clinical trial failure rates remain high. This review examines the transformative role of
emerging technologies in Alzheimer’s disease research, focusing on brain organoids and organ-on-chip
systems. By generating brain organoids from human pluripotent stem cells, researchers can develop
pathological features in a three-dimensional context. These models reveal features such as amyloidbeta
deposition, tau pathology, and synaptic dysfunction. In parallel, organ-on-chip platforms utilize
microfluidic systems to simulate physiological conditions. This enables the study of cellular interactions,
including those involving the blood–brain barrier. By integrating these approaches, researchers acquire
more relevant human models. This combined strategy deepens mechanistic insight and enhances drugscreening
accuracy. It also brings new opportunities to explore innovative treatment methods. With such
tools in hand, early disease processes can be investigated—sometimes even before clinical symptoms
emerge. However, challenges remain, including limited tissue complexity and enduring technical issues.
Nonetheless, as progress continues steadily, these prototypes are becoming increasingly practical.
Such advances bring significant potential for Alzheimer’s disease research. They offer hope for earlier
diagnosis, improved treatments, and more individualized care.
| 48 |
Author(s):
Kavin Srinivasan, Claudia D’Ettorre.
Page No : 420-425
|
Advancing Minimally Invasive Surgery with Magnetically Actuated Surgical Instrument
Abstract
Minimally Invasive Surgery (MIS) has profoundly impacted the field of patient care, although it still
faces the challenges of limited dexterity and the constraints of distance posed by conventional surgical
tools. The use of Magnetically Actuated Surgical Instruments (MASI) represents an exciting innovation
capable of wireless, accurate manipulations in the complex, small spaces of the human body. The current
literature on MASI developments from 2010 to 2025 in gastrointestinal, neurosurgical, vascular, and
Magnetic Resonance Imaging (MRI)-guided MASI applications was assessed. Electromagnetic MASI
offer the benefits of programmable actuation, precision, and real-time imaging, enabling the surgeon’s
intention to be executed with reduced tissue trauma. While issues of accurate localization, motion
prediction in soft tissues, and installation costs, among others, remain, ongoing efforts in automation,
force feedback, and image guidance appear to be addressing existing hurdles. MASI represents important
technology in the development of next-generation surgical robots or patient-focused surgical approaches.
| 49 |
Author(s):
Zihao Ye.
Page No : 426-432
|
Evaluating Bias in Machine Learning Predictions of High School Students’ Academic Performance in Ontario, Canada
Abstract
This study examines whether machine learning models trained on aggregated institutional data
exhibit varying prediction accuracy among different school performance groups within Ontario’s
public education system. Utilizing theoretical frameworks from algorithmic fairness research, the study
investigates whether predictive reliability exhibits systematic variation across educational contexts, even
in the absence of explicit demographic variables. Using publicly available standardized test data from
the Education Quality and Accountability Office, the study created regression models to predict how
well schools perform academically based on their achievement levels. Model performance was evaluated
using error-based metrics and subgroup error analysis across performance strata. Results indicate that
prediction accuracy was not uniform across school groups, with lower-performing schools consistently
exhibiting higher prediction error across model configurations. These results show that predictive models
created from combined educational data might perform differently depending on the specific context.
This underscores the necessity of assessing predictive consistency across institutional contexts when
implementing machine learning techniques in education.
| 50 |
Author(s):
Dev Narain Kushangi.
Page No : 433-438
|
Black Hole Formation from Massive Star Collapse: A Review of Physical Mechanisms and Observational Evidence
Abstract
This review examines the formation of stellar-mass black holes as the endpoint of massive star
evolution. As massive stars progress through successive stages of nuclear burning, they develop iron cores
that can no longer be supported under typical stellar conditions by thermal pressure or electron degeneracy
pressure. This ultimately leads to gravitational collapse under standard stellar evolution models. Collapse
of this core can result in either a neutron star or a black hole, depending on a combination of factors such
as metallicity, mass loss, rotation, binary interaction, shock revival mechanisms, and fallback accretion.
Recent neutrino-driven core-collapse supernova simulations indicate that black holes do not occur above a
single mass threshold, but instead along complex regions characterized by “islands of explodability” and
failed-explosion pathways. This review combines theoretical and computational models of core collapse,
failed explosion, fallback collapse to black holes, and direct collapse, and connects these processes to
observational constraints from X-ray binaries, supernova remnants, gravitational-wave observations, and
multimessenger astrophysics. Significant uncertainties remain, particularly regarding the remnant mass
distribution, the importance of multidimensional effects, rotation, magnetic fields, and binary evolution.
Black hole formation therefore remains a central problem linking stellar evolution, supernova theory,
compact-object demographics, and strong-gravity astrophysics.
| 51 |
Author(s):
Madison Park, Chelsea Harmon.
Page No : 439-453
|
Reward and Impulse: Leveraging a Neurobiological Bidirectional Framework for Understanding ADHD and Social Media Addiction among Adolescents
Abstract
The rapid rise of social media use among adolescents has raised concerns for parents and researchers
about its addictive potential and detrimental mental health consequences, particularly for individuals with
Attention-Deficit/Hyperactivity Disorder (ADHD), a population characterized by reward dysregulation
and impaired impulse control. This literature review examines the neurobiological vulnerabilities
underlying ADHD and explains how these likely increase the susceptibility to social media addiction, as
well as how addictive social media may in turn exacerbate ADHD symptomology, through dysregulated
dopaminergic reward pathways and deficits in behavioral inhibition. The review proposes a bidirectional
relationship between ADHD and social media addiction, arguing that social media platforms both exploit
and exacerbate ADHD-related vulnerabilities to result in negative mental health outcomes. To illustrate
this interaction, specific design features of Instagram, Snapchat, YouTube, and TikTok are considered to
demonstrate how distinct and overlapping mechanisms of reinforcement, urgency, and personalization
promote compulsive engagement with social media platforms for adolescents with ADHD diagnoses.
Finally, the review synthesizes evidence linking the proposed bidirectional relationship between ADHD
and social media addiction to adverse mental health outcomes, including anxiety, depression, and sleep
disturbances among adolescents. Taken together, interpreting the growing body of research in this way
suggests that reward dysregulation and impulse control underline the bidirectional relationship between
ADHD and social media addiction, resulting in increased risk for poor mental health. Despite limitations
of existing research, the proposed integrated bidirectional framework can help to guide future research,
clinical interventions, and the development of social media platforms to better support adolescent mental
health, especially among neurodivergent populations.
| 52 |
Author(s):
Sabrina Kiung.
Page No : 454-464
|
Emerging Innovative Cell-Based Wound Dressings for Scarless Wound Healing
Abstract
Effective wound healing is critical for restoring both the function and appearance of damaged skin,
yet chronic wounds and visible scars remain a major physical and emotional challenge. Conventional
dressings primarily serve as physical barriers that prevent infection and maintain a healing-promoting
environment, but do little to stimulate true tissue regeneration. Emerging research in regenerative
medicine has led to the development of cell-based wound dressings, combining live cells, such as
keratinocytes and stem cells, with engineered support materials. These innovations promote the release
of biological signals that reduce inflammation and guide collagen organization (crucial to effective
and scarless healing), resulting in more functional and aesthetically improved outcomes. In particular,
adipose-derived stem cells (ASCs) and bone marrow-derived stem cells (BMSCs) have demonstrated the
ability to accelerate wound closure and minimize fibrosis by producing regenerative signals at the wound
site. Dressings arising from the natural regeneration of fetal skin repair provide further direction for
designing future therapies that promote complete scarless healing. Although fetal-cell-based treatments
display strong potential, ethical and practical challenges remain. This paper reviews current progress in
cell-based wound dressings and explores how insights from fetal healing could help achieve faster, more
effective, and scarless wound repair in the future.
| 53 |
Author(s):
Claire Daeun Kim.
Page No : 465-477
|
Explaining Human-AI Interaction: An Experimental Study of Transparency and Cognitive Load in AI Decision-Making
Abstract
As artificial intelligence systems increasingly assist or replace human decision-making in high-stakes
domains, transparency has become a central design principle intended to support user understanding and
trust. However, the effectiveness of transparency may diminish as users may not process the provided
information entirely due to cognitive load. Thus, it is unclear whether detailed explanations of AI
systems always lead human users to evaluate the system in a positive light. To answer the question, this
study conducted an online experiment where 181 participants were given AI-assisted decision-making
scenarios with different levels of decision transparency and cognitive load. Contrary to the expectation
that transparency may backfire under high cognitive load, the findings show that transparency about the
decision process consistently increased perceived trustworthiness and ethicality of AI systems regardless
of cognitive load. Moreover, the experiment results find that the level of explanation detail or cognitive
load does not alter users’ responsibility attribution, pointing to the rigidity of moral responsibility in the
AI-assisted decision-making contexts. This study contributes to the literature of AI-human interaction
by empirically demonstrating the limited role of cognitive load in shaping human perceptions of AI
decision-making. In addition, this study hints at important practical implications for how AI-human
interaction should be designed to foster the effectiveness of the interaction.
| 54 |
Author(s):
Jay Tamirisa, Jackson Hardin .
Page No : 478-485
|
Social Robots in Service Contexts: Design, Personalization, and Real-World Applications
Abstract
Social robots (SRs) interact with humans using speech, motion, and affective behavior, and are
used in retail, education, healthcare, and disability services. Advances in big data, machine learning,
and generative artificial intelligence have enhanced the responsiveness of SRs. However, real-world
adoption remains inconsistent, and key human-robot interaction (HRI) dynamics, such as adaptability,
personalization, emotional modeling, and robot ‘personality,’ are underexplored. This narrative review
synthesizes cross-sector evidence to identify patterns, limitations, and emerging design principles. SRs
are most effective when integrated into existing workflows and tailored to context. Socially expressive
SRs enhance engagement in hospitality, whereas emotionally responsive designs are more effective for
children with autism. In healthcare, SRs are more readily accepted as complementary team members,
rather than human substitutes. Overall, effectiveness depends on alignment between system design
and user context. Persistent challenges, including novelty effects, ethical concerns, and limited longterm
validation, constrain scalability, highlighting the need for human-centered, interdisciplinary
development.
| 55 |
Author(s):
Keshav Raghavan.
Page No : 486-498
|
Sustainability Finance for SMBs in the United States: Unlocking Capital for Green Innovation
Abstract
Small and medium-sized businesses (SMBs) account for 90% of enterprises in the United States and
contribute significantly to the national economy. However, they encounter major challenges in accessing
capital for sustainability projects and green innovation that could hinder progress toward the U.S.
government’s climate goal of achieving net-zero greenhouse gas emissions by 2050. This study draws on
original survey data from 42 SMBs across multiple industries in South Florida and finds a significant gap
between SMBs’ interest in sustainability finance and their ability to access available resources. While
90% of surveyed SMBs expressed interest in funding sustainability projects, all respondents identified
complex financial channels as a major deterrent. Moreover, only 20% had any knowledge of federal
and state-sponsored loan programs that incentivize green investments. Using a combination of primary
survey data, secondary policy and market research, this study reveals that barriers to SMB sustainability
finance are more informational and structural than capital-related. Additionally, the findings of this
research highlight the importance of targeted interventions to improve access to sustainability finance
that can help unlock green innovation potential for American SMBs in the context of national climate
goals and economic competitiveness.
| 56 |
Author(s):
Elston Su.
Page No : 499-503
|
A Review of AI Safety and Trustworthiness in Autonomous Vehicles
Abstract
This narrative review examines how autonomous-vehicle safety depends on the combined roles of
perception, robustness, explainability, and ethical alignment. It argues that reliable perception and robust
model behavior form the technical foundation of safe decision-making, while explainability enables
transparency, accountability, and system-level oversight. In addition, ethical considerations, including
fairness, responsibility, and bias mitigation, are shown to be inseparable from safety in AI-driven driving
systems. This article reviews recent research, comparing key technical challenges, robustness limitations,
and interpretability requirements across autonomous driving architectures. The review identifies major
gaps in existing work, including limited robustness under rare and unpredictable scenarios, insufficient
dataset diversity, and the absence of unified safety and certification frameworks. It concludes that both
industry and regulatory bodies must adopt stronger certification practices and responsible deployment
strategies to ensure that autonomous-vehicle systems remain trustworthy, transparent, and safe.
| 57 |
Author(s):
Taehong Kim, Jiaen Wang, Jose Jimenez, Judy Song, Taehong Kim, Yechan Park.
Page No : 504-510
|
Using Acid/Base with AHAPTMOS to Control the Hydrophilic/Hydrophobic Character of Silicon Wafers
Abstract
In the semiconductor industry, manufacturing processes require varying degrees of hydrophobicity/
hydrophilicity in silicon wafers. To optimize transitions between these processes, the effectiveness of
using acid/base to control the hydrophobicity/hydrophilicity of a silicon wafer following the addition
of N-(6-aminohexyl)aminopropyltrimethoxysilane (AHAPTMOS) was examined. After coating with
AHAPTMOS, the untreated wafer will become more relatively hydrophobic, but when acid is applied,
the surface amino groups are protonated, theoretically increasing hydrophilicity; applying a base would
neutralize the surface and reverse this process. A total of four trials were conducted, each trial consisting
of four successive tests for a single wafer: No Coating, the control group, with the uncoated/untreated
wafer; Coated But Not Treated, with AHAPTMOS applied to the same wafer; Acid Treated, with weak
acid applied to the coated wafer; Base Treated, with a weak base applied to the acid-treated wafer.
For each test, the contact angle was measured with a goniometer and the Ossila computer software.
Results indicated that the hydrophobicity of the wafer was successfully manipulated: upon application of
AHAPTMOS, the contact angle experienced a 46.18% increase from the control, and with the subsequent
addition of acid, the contact angle yielded a 13.60% decrease. Then, the addition of a base resulted
in a 23.14% increase in contact angle. These results have strong implications for future semiconductor
research and manufacturing, as AHAPTMOS can be explored as a more efficient coupling agent to
control the hydrophilicity/hydrophobicity of silicon wafers.
| 58 |
Author(s):
Eugene Yang.
Page No : 511-515
|
Comparative Review of Fire Growth Models
Abstract
Climate change resulted in an increase in wildfire frequency and severity, with hotter climates
and drier weather. Along with the wildfire frequency, there is a growing need for wildfire prevention
systems and infrastructure. Fire growth models (FGMs) assist in predicting fire behaviors and
developing effective countermeasures. This paper reviews various FGMs including Fire Area Simulator
(FARSITE), Prometheus, and Cell2Fire, analyzing their methods and doing comparative analysis on
their computations and practical uses. Prometheus and FARSITE rely on wave-propagation, yielding
results that closely align with real-life wildfire cases. Each model is tailored to its respective region:
Prometheus for Canada and FARSITE in the United States. Accuracy is highly dependent on quality
of data, particularly wind and fuel maps, which is why they are noticeably less accurate in testing in
other regions. Cell2Fire is much faster compared to Prometheus and FARSITE due to its use of cellular
automata for faster parallel computing, however with slightly reduced accuracy.
| 59 |
Author(s):
Yash Garg.
Page No : 516-521
|
Implications of Adolescent Absenteeism, Causes, and Long -Term Outcomes
Abstract
Adolescent absenteeism is a rising issue in society today; more and more, students are missing school
which has the potential to cause detrimental effects on the livelihoods of these students in the short and
long term. Research shows that it is not always a behavioral issue; sometimes, students have deeper issues
that are affecting their life and routine. Adolescents are often dealing with stress, mental health problems,
and family issues. Furthermore, research shows that whether students feel safe and welcomed in their
school’s environment plays a huge role in absenteeism. Only a couple of absences have the potential to
initiate a pattern that can change the outcomes of opportunities, social life, and life in general for these
students. Missing school affects more than grades, students who are out a lot often fall behind and lose
confidence. Students with frequent absences may also have a harder time maintaining friendships. These
struggles can continue after high school leading to potential problems in many aspects of life, however,
research consistently indicates that early support works in preventing absenteeism. When school staff
or administrations treat absenteeism as an issue that extends beyond behavioral problems, students do
better. This is because schools can take the time to build trust, talk with families, and determine the root
cause. It is therefore critical that the help is individualized to match the students’ needs. When schools
step in early and stay consistent, adolescents have a better chance of staying connected and on track
which has benefits that last beyond school.
| 60 |
Author(s):
Hanzhi Zhang.
Page No : 522-532
|
Prevention and Control of Canine Parvovirus
Abstract
Since its discovery in 1978, Canine Parvovirus (CPV-2) has become one of the most life-threatening
pathogens for dogs. The high mutation rate of CPV-2 has led to the rapid emergence of three main
variants and widespread transmission. Therefore, the prevention and control of CPV is important.
CPV vaccination is the mainstay for the prevention of the disease in dogs. However, the efficacy of
vaccine can be undermined by the poor coverage and the maternally derived antibodies (MDAs). This
article summarizes the current status of CPV prevention and control, including vaccines, methods of
disinfection, diagnosis, and potential treatment, with a focus on vaccines. It outlines the development of
vaccines, their efficacy, and drawbacks. The review also discusses the different methods for diagnosis of
the disease and how to inactivate the virus in the environment using disinfectants. This article aims to
provide a comprehensive overview of CPV control to enhance public awareness of CPV.
| 61 |
Author(s):
Harshatej Simhadri.
Page No : 533-547
|
Machine Learning-Enhanced Radiomics in Neuro-Oncology: A Systematic Review of Predictive Models Beyond RANO Criteria
Abstract
The Response Assessment in Neuro-Oncology (RANO) criteria are widely used for assessing
treatment response in brain tumours; however, they have recognized limitations in differentiating true
tumour progression from treatment-related imaging effects such as pseudo-progression. Radiomic
analysis enables non-invasive evaluation of tumours by extracting numerous quantitative features from
medical images, thereby revealing imaging characteristics that may not be detectable through standard
visual interpretation. This systematic review evaluates existing evidence on machine learning-enhanced
radiomic applications in neuro-oncology, specifically the prediction of treatment response, molecular
marker characterization, and survival prognostication. The quality of the selected studies was assessed
using the QUADAS-2 tool and the TRIPOD guidelines for prediction model studies. Data synthesis was
conducted in accordance with PRISMA 2020 guidelines. There were 12,847 patients across 63 studies
who met all the inclusion criteria. Multiparametric radiomic models incorporating shape, intensity, and
texture features derived from T1-weighted, T2-weighted, and FLAIR sequences demonstrated higher
reported performance metrics across all clinical applications. Nevertheless, there was substantial
heterogeneity in the feature extraction protocols, the implementation of validation strategies, and
approaches to model interpretation. Integrating machine learning techniques with radiomic feature
analysis has become an advancing approach in precision neuro-oncology, often showing improved
predictive accuracy compared with traditional evaluation strategies in multiple clinical settings.
Successful clinical implementation will depend on standardized imaging acquisition practices, rigorous
validation across multiple institutions, and the development of transparent and interpretable modelling
approaches. Key future directions involve conducting prospective clinical studies, applying federated
learning approaches, and incorporating these models into clinical decision-support platforms.
| 62 |
Author(s):
Alison Wang.
Page No : 548-559
|
Comparing Recent 3D Bioprinting Methods and Bioinks in Replicating the Structural and Functional Complexity of Native Cardiac Tissue
Abstract
After cardiac injury, such as a heart attack, it is difficult for cardiomyocytes to regenerate. Recently,
3D bioprinting (3DBP) has become increasingly explored for its methods and bioinks to repair or restore
damaged myocardium. 3DBP precisely places cells and biomaterials into cardiac architectures that aim
to resemble the structural complexity of native cardiac architectures, as well as mimic the functionality,
which includes levels of cell viability, electrical conductivity, vascularization, and modulus. There are
several main methods of 3DBP, such as extrusion-based, which uses a nozzle, or stereolithography, which
uses light. These methods vary in the regulation of other factors, including resolution, bioink viscosity,
and multi-material compatibility. Furthermore, the materials chosen for bioinks directly determine
cell interactions with one another and with the surrounding extracellular matrix, and are typically
selected in accordance with the geometry of the targeted cardiac structure. Natural bioinks offer high
biocompatibility; synthetic bioinks have improved printability and stiffness; and hybrid bioinks can
provide the advantages of both, but come at the cost of more complex formulations. The purpose of this
review is to provide a comprehensive overview of these methods and materials, and compare these in
terms of their individual advantages when applied to the cardiac structure most compatible with their
features. Overall, this information will be critical to aid researchers in developing novel cardiac tissue
engineering treatments.