| 1 |
Author(s):
Sihoon Kim.
Page No : 1-6
|
An Empirical Evaluation of DragGAN’s Efficacy Across Distinct Subject Categories
Abstract
Generative Adversarial Networks (GANs) have reshaped the landscape of synthetic media, enabling
the creation of hyper-realistic imagery through adversarial learning. Within this domain, DragGAN
has emerged as a notable innovation, offering intuitive point-based manipulation of generated images
by translating user-specified handle points to target spatial locations. Despite its qualitative success in
published demonstrations, a rigorous quantitative evaluation of its performance across varying semantic
categories remains absent from the literature. This study addresses that gap by assessing DragGAN’s
efficacy in maintaining structural integrity and generating diverse outputs across four distinct subject
categories: human faces, dog faces, cat faces, and whole dog bodies. Using a curated dataset of images
generated from pre-trained StyleGAN2 checkpoints (FFHQ, AFHQ, and a Self-Distilled StyleGAN
body model), the Structural Similarity Index (SSIM) was applied to measure fidelity and a decomposed
Inception Score (IS) was used to evaluate perceptual quality and diversity. All categories exhibited
substantial structural degradation under point-based manipulation, with mean SSIM scores ranging
from 0.21 (cat faces) to 0.33 (dog bodies). The full-body dog category achieved the highest structural
preservation, while facial categories—particularly cat and dog faces—showed the greatest degradation.
Decomposed Inception Score analysis indicated consistently low classifier confidence across all
categories, a pattern attributable to domain mismatch between the generated subjects and the ImageNettrained
Inception-v3 classifier. These findings establish a quantitative baseline indicating that DragGAN’s
point-based manipulation introduces significant structural distortion across all tested domains, with
relative performance differences suggesting that full-body manipulation may be more tractable than finegrained
facial editing.
| 2 |
Author(s):
Faith Koh.
Page No : 7-15
|
Sex Differences in Thermoregulation During Exercise in Hot-Humid Environments: A Narrative Review with Implications for Athletic Populations in Singapore
Abstract
This narrative review synthesizes current evidence on sex differences in thermoregulatory responses
during exercise, with specific application to athletes training in Singapore’s hot-humid climate. The
review examines physiological mechanisms underlying sex-based differences in sweating, cutaneous
vasodilation, and hormonal influences on thermal regulation. Evidence indicates that males generally
exhibit higher sweat rates and earlier sweating onset, whereas females demonstrate lower sweat output
per gland despite higher gland density, with additional thermoregulatory variability introduced by
menstrual cycle phases. In hot-humid environments characteristic of Singapore, reduced evaporative
cooling efficiency may attenuate these sex differences, as high ambient humidity limits heat dissipation
regardless of sweat production capacity. This review evaluates Singapore’s athletic heat stress
frameworks, particularly Sport Singapore’s Heat Stress Management Plan, identifying gaps in sexspecific
provisions. Recommendations include individualized hydration guidelines based on sweat rates,
educational initiatives regarding menstrual cycle effects on thermoregulation, and Singapore-specific
research to validate laboratory findings in field conditions.
| 3 |
Author(s):
Blossom Patel, Hangpeng Li.
Page No : 16-19
|
The Biological Significance of cfDNA Methylation Patterns in Early Ovarian Cancer Detection and Analytical Methods for Detection
Abstract
Ovarian cancer is one of the deadliest gynecological cancers; survival over five years drops sharply,
from about 90% when found early to under 30% if detected late. Although ovarian cancer detection based
on blood tests that analyze cell-free DNA (cfDNA) could provide a less invasive option, finding consistent
signals in the limited data found in blood samples is difficult. To address the high-dimensional nature
of this data, the critical challenge of data leakage in machine learning pipelines was investigated. Taken
together, combining sophisticated AI methods with highly sensitive methylation tests appears to offer the
best chance for developing practical early-detection screening tools. A synthetic dataset was generated
that mirrors plasma cfDNA methylation fragments, and a simulation was performed to determine the
number of cancerous versus noncancerous differentially methylated regions (DMRs). First, a standard
“global” selection model was simulated, which displayed the artificial inflation (overfitting) of accuracy
that occurs due to data leakage. Second, a nested-CV elastic-net model was tested to isolate the true
biological signal from cfDNA methylation. After modeling this procedure, the leakage-safe model could
successfully distinguish early ovarian cancer from non-tumor samples (AUROC 0.938; AUPRC 0.914).
This project lays the foundation for future exploration of theory-driven, AI-powered liquid biopsy models.
| 4 |
Author(s):
Emily Avram, Rakesh Chand.
Page No : 20-27
|
Global Surveillance Evidence of Rabies as a Threat to Wildlife Conservation
Abstract
Rabies is a fatal zoonotic disease that is widely recognized as a public health priority, yet its
implications for wildlife conservation remain insufficiently examined. This study assessed whether
rabies constitutes a conservation threat by analyzing international surveillance data alongside a narrative
synthesis of published evidence on urban–sylvatic transmission. Reported wildlife rabies deaths were
extracted from the World Organisation for Animal Health’s World Animal Health Information System
(WAHIS) to describe long-term global trends from 2005 to 2024 and recent regional patterns from 2020
to 2024, with species-level records mapped to International Union for Conservation of Nature Red List
categories. Surveillance data showed that wildlife rabies deaths have been consistently reported across
all major global regions over the past two decades, with a general decline in reported deaths since 2012.
Between 2020 and 2024, 4,850 wildlife rabies deaths were reported, including 53 deaths (1.09%) in species
classified as endangered, critically endangered, or vulnerable, encompassing multiple taxonomic groups
and geographic regions. Although the proportion of reported deaths in conservation-priority species was
small, their distribution across vulnerable taxa indicates that rabies exposure extends into populations
where even limited mortality may have disproportionate conservation consequences. Interpretation of
these findings is constrained by substantial underreporting and uneven surveillance capacity, particularly
in resource-limited settings. Overall, the results indicate that rabies represents an underrecognized but
meaningful risk to wildlife conservation, especially at domestic animal–wildlife interfaces, and that
integrating rabies control measures, particularly mass dog vaccination, with conservation planning and
surveillance within a One Health framework may support biodiversity protection while advancing global
rabies elimination efforts.
| 5 |
Author(s):
Thondari Cho Thar.
Page No : 28-37
|
Organizational Reconfiguration in Response to Technological Disruption: A Case Study of Microsoft Under Satya Nadella
Abstract
When Satya Nadella became CEO of Microsoft in 2014, the company faced a defining shift: moving
from desktop software to cloud computing. This case study examines how Microsoft transformed
between 2014 and 2018, applying theories of dynamic capabilities, organizational ambidexterity, and
identity to understand how real change happens. The findings show that Microsoft recognized the shift
to the cloud, invested in Azure, and restructured its workforce. Change spread through distributed
adaptation, where employees at all levels experimented and learned. The Stratus team served as a teacher
model, building skills within departments rather than isolating innovation. Microsoft’s identity shifted
through daily work as identity emerged from practice; new habits reshaped how the company saw itself.
Underlying these changes was psychological safety, the trust that enabled people to take risks. This
study contributes to adaptation theory by introducing distributed adaptation, questioning the assumption
that ambidexterity requires separate units through a teacher model, and suggesting that organizational
identity can emerge from behavioral change rather than precede it. The results show that lasting change
arises from habits embedded in workplace culture, offering useful insights for organizations facing
technological disruption.
| 6 |
Author(s):
Aarav P. Narang.
Page No : 38-43
|
The Gut–Brain Axis and Insulin Resistance: Evaluating Alzheimer’s Disease as Type 3 Diabetes
Abstract
Alzheimer’s disease is a progressive neurodegenerative disorder characterized by memory loss and
cognitive decline. While traditionally associated with amyloid-beta plaques and tau protein tangles,
recent research suggests that metabolic dysfunction, particularly impaired insulin signaling in the brain,
may play a central role in its development. This supports the hypothesis that Alzheimer’s disease may
represent “Type 3 Diabetes.” At the same time, the gut–brain axis has emerged as an important system
linking gut microbiota to brain function through immune, metabolic, and neural pathways. This paper
examines how gut microbiome dysbiosis may contribute to insulin resistance and Alzheimer’s disease.
Evidence suggests that dysbiosis promotes inflammation and disrupts insulin signaling, but causality
remains unclear.
| 7 |
Author(s):
Raudeen Roodgarmi.
Page No : 44-51
|
Measuring AI Accuracy on Standardized Tests: A Comparative Study of ChatGPT, Copilot and Gemini
Abstract
This study evaluates the performance of three widely used artificial intelligence systems, ChatGPT,
Microsoft Copilot, and Google Gemini, on standardized test questions in Math, Reading, and English
derived from SAT and ACT examinations. A total of 90 questions (30 per subject) were selected from
multiple test forms across different years to reduce potential bias and ensure broad content coverage. All
questions, including those with visual components, were presented to each AI system in a standardized
format, and responses were scored for accuracy. A chi-square test for homogeneity was conducted
to assess differences in performance among the models. Results indicate that all three AI systems
performed strongly in language-based tasks, Reading and English. In contrast, performance in Math was
notably lower across all models, with common errors involving advanced mathematical concepts and
misinterpretation of visual and graphical information. Despite observable differences in error patterns,
statistical analysis revealed no significant differences in overall performance among the three systems.
These findings suggest that current AI models are highly proficient in processing and interpreting
textual information but remain less reliable in mathematical reasoning and multimodal tasks. The study
highlights both the capabilities and limitations of AI in standardized testing contexts and underscores
the importance of prompt design and continued model development.
| 8 |
Author(s):
Aarit Das.
Page No : 52-61
|
Dynamic Multi-Asset Portfolio Optimization: Evaluating Risk-Return Tradeoffs Under Time-Varying Volatility
Abstract
Prior literature on the low-volatility anomaly suggests that portfolios composed of lower-volatility
assets often achieve superior risk-adjusted returns compared to their higher-volatility counterparts over
long investment horizons. This phenomenon challenges the traditional risk-return trade-off implied by
classical asset pricing models, which associate higher risk with higher expected returns. As a result, lowvolatility
strategies have gained attention for their ability to deliver more efficient return profiles with
reduced downside risk. This study investigates this claim by comparing alternative portfolio allocation
strategies in optimizing the risk-return tradeoff over the 2021-2025 period. Using volatility forecasts
generated through a GARCH (1,1) model and evaluating two allocation frameworks - volatility targeting
and Sharpe ratio-constrained optimization, we examine performance across varying market conditions.
The findings indicate that Sharpe ratio-constrained optimization produces higher returns during strong
market recoveries; however, it is vulnerable to significant drawdowns and elevated tail risk during market
downturns. In contrast, the volatility-targeting strategy demonstrates greater stability, lower maximum
drawdowns, and more consistent risk-adjusted performance in adverse market environments. Overall,
the results suggest that dynamic rebalancing and active risk management are critical determinants of
long-term portfolio performance. High-volatility assets, such as Bitcoin, may enhance returns, but only
when their exposure is carefully managed within a diversified portfolio framework.
| 9 |
Author(s):
Aarav Gupta.
Page No : 62-68
|
Structural Drivers of U.S. Drug Price Disparities: Lessons from Germany and the Role of AI in Reform
Abstract
The United States leads the world in pharmaceutical innovation, yet it has one of the highest drug
prices globally, creating significant barriers to equitable healthcare access. This paper aims to examine
the structural mechanisms that drive drug price disparities between the United States and Germany,
and to evaluate the potential of artificial intelligence as a complement to pharmaceutical pricing reform.
This comparative analysis demonstrates that Germany’s centralized pathways, including early benefit
assessment, reference pricing, and mandatory price negotiations under the AMNOG framework, constrain
launch prices and accelerate post-exclusivity generic competition and price erosion. In contrast, the U.S.
system is characterized by fragmented payer negotiation, legal hurdles such as the Medicare Part D
non-interference clause, and direct-to-consumer advertising that steers consumers toward higher-priced
brand-name drugs. The study evaluates proposals to enable centralized price negotiation and broader
biosimilar access and considers emerging AI-enabled efficiencies in drug research and development to
reduce drug development costs. Together, these findings suggest a multi-pronged framework for lowering
drug costs and increasing access to healthcare while sustaining incentives for innovation.
| 10 |
Author(s):
Maya Saltzman.
Page No : 69-77
|
The Importance of Food in Sri Lankan Cultural, Religious, and Ritual Practices
Abstract
This paper explores the significance of food in Sri Lankan ritual contexts, arguing that certain foods
have a unique flexibility and role in ritual ceremonies. Previous research has shown that food in Sri
Lankan culture plays an active role in ritual practices. Building on these works, this paper argues that
ritual foods exist along a “spectrum of significance”, in which their symbolic meaning varies according
to their dependence on ritual context: some foods derive meaning primarily through participation in
specific rituals, while others retain broader cultural significance across both sacred and secular domains.
This study draws on face-to-face interviews with five participants, all born and raised in Sri Lanka,
but now living in New Jersey, USA. In their interviews, each participant discussed their relationships
with food, rituals, and spiritual influences, particularly regarding kiribath, rice, honey, and milk. The
interview data exemplify that a variety of foods are significant and symbolic, but they are positioned
on a spectrum of significance, where some hold significance on their own, and others gain importance
through involvement in a ritual. This concept adds to current scholarship by offering a more nuanced
methodology to interpret differences in ritual food practices.
| 11 |
Author(s):
George Tai Zhao.
Page No : 78-85
|
High-Resolution Spectral Analysis and Luminous Efficiency Evaluation of Various Light Bulbs
Abstract
Addressing climate change requires lighting technologies that are both energy-efficient and safe
for human health. This work systematically evaluates four widely used lighting types—incandescent,
halogen, fluorescent, and light-emitting diode (LED) lamps—using a high-resolution spectral observation
instrument, which covers the wavelength range of 300–1000 nm with a spectral resolution of 0.45 nm.
The spectral measurements are further analyzed to determine the distribution of emitted energy across
visible (380–780 nm), ultraviolet (UV, <380 nm), and high-energy blue-light bands (415–455 nm). Results
show that incandescent and halogen lamps exhibit low luminous efficacy (~15%) but negligible UV and
blue-light emissions. In contrast, fluorescent lamps achieve higher efficacy (92–99%) but emit measurable
UV (1–2%) and blue light (4–20%), indicating potential exposure risks. LEDs offer the highest efficacy
(~99%) with no detectable UV emission; however, 11–15% of their output falls within the high-energy
blue-light range, raising health concerns under prolonged exposure. These findings provide a quantitative
basis for designing lighting technologies and standards that integrate both sustainability and health
considerations.
| 12 |
Author(s):
Angela Han.
Page No : 86-97
|
Impact of Vitamin D Production on Skin Cancer Risk: Associations With Dietary Intake and Geographical Factors
Abstract
Cutaneous malignant melanoma (CMM) and non-melanoma skin cancers (NMSC), including basal
cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most frequent types of cutaneous
cancer. NMSC diagnoses comprise more than one-third of all cancers. Ultraviolet (UV) exposure is
a primary requirement to produce vitamin D for individuals. However, this exposure is accompanied
by an increased risk of skin cancer. Some studies have observed that vitamin D synthesis may protect
against skin cancer, but the relationship remains debated in the scientific literature. Alongside studies
that suggest a protective role of vitamin D in skin cancer, there have also been observations related to the
connection between vitamin D derived from other sources such as dietary and environmental factors,
and cancer risk. The role of dietary habits and nutrient intake in skin cancer risk has gained attention in
recent years, as the two naturally occurring forms of vitamin D, ergocalciferol and cholecalciferol, are
found in food. However, the association between dietary vitamin D intake and skin cancer risk remains
controversial. Current findings lack clarity regarding whether sun-induced vitamin D production varies in
locations observed to be at a higher risk for cancer due to geographical factors. There has been difficulty
examining an independent influence of vitamin D status on skin cancer risk due to confounding and
contrasting effects of sun exposure and other factors such as dietary vitamin D. Additional research
is needed to confirm the preventive role of vitamin D in skin cancer risks, and to eliminate potential
confounding variables.
| 13 |
Author(s):
Arnav Rajadhyaksha.
Page No : 98-106
|
Evaluating Dynamic Investment Scaling in Pairs Trading Across U.S. Equity Sectors
Abstract
This study evaluates whether dynamic investment scaling based on spread magnitude enhances the
performance of pairs trading strategies. Using daily closing prices of U.S. equities across eleven sectors
from 2021 to March 2026, pairs are identified through a two-stage selection process combining zerocrossing
frequency and the Augmented Dickey–Fuller (ADF) tests applied to rolling z-score–normalized
spreads. Pairs are formed over a 12-month formation period and evaluated over the subsequent 3-month
trading windows. Trading rules incorporate threshold-based entry, exit, and stop-loss conditions, while
position sizes are adjusted dynamically using a parameterized scaling function (k-values) as spreads
change. Sensitivity analysis suggests that intermediate entry thresholds (z ≈ 1.25) balance trade
frequency and signal quality, while wider stop-loss thresholds (z ≈ 3.5) help mitigate extreme losses.
Results show that no single scaling parameter consistently maximizes returns across sectors or time
periods. Regression analysis indicates that scaling parameters are not statistically significant predictors
of returns, whereas ADF statistics and selected sector indicators exhibit significance. However, the
explanatory power remains limited (adjusted R² ≈ 0.03), consistent with the noisy nature of financial
returns. In contrast, scaling parameters are significantly associated with maximum drawdown, suggesting
an effect on downside risk rather than expected returns. Overall, dynamic investment scaling does not
materially improve returns but can reduce drawdowns. Strategy performance is more strongly driven by
pair selection characteristics, particularly spread stationarity, and sector-specific factors. These results
should be interpreted with caution given simplifying assumptions such as excluding transaction costs
and relatively limited out-of-sample evaluation data.
| 14 |
Author(s):
Jiyun Lim.
Page No : 107-123
|
Dietary Patterns and Anxiety Symptoms: Population-Level Evidence from the 2024 Korea National Health and Nutrition Exam ination Survey
Abstract
This study explores the associations between dietary habits and anxiety disorders at the population
level. Although there has been increasing scholarly attention to the biological mechanisms, such as the
gut-brain axis, and social mechanisms, such as socio-economic status, through which anxiety disorders
emerge, few empirical studies have examined the dietary factors that may improve or impair anxiety
disorders at the population level. Thus, this study hypothesizes that several risk and protective factors
related to dietary habits are strongly associated with anxiety disorders and tests this relationship using
data from the 2024 Korea National Health and Nutrition Examination Survey (KNHANES), published
by the Korea Disease Control and Prevention Agency, consisting of the final analytical sample of 4,406
respondents. The regression analyses with multiple models reveal no statistically significant associations
between nutrient-level diet factors, such as daily intake of saturated fat, omega-6 fatty acid, sodium,
sugar, omega-3 fatty acid, and fiber, and anxiety symptoms. However, the results also reveal consistent
significance of some socioeconomic, health, and behavioral factors, including income, health access,
health literacy, smoking, and sleep, which all exhibited p-values less than 0.05 across different models.
These results are discussed from the perspective of the fundamental cause theory. Key improvements in
research design and data collection to test the associations between diet and anxiety at the population
level are discussed. Also, the broader implications of how public health policy should intervene in the
rising prevalence of anxiety are presented.
| 15 |
Author(s):
Blake E. Matz.
Page No : 124-132
|
Wavelet-Integrated Machine Learning Models for Predicting Marine Chlorophyll-a Concentration along the California Coast
Abstract
In recent years, algal blooms have occurred with greater frequency and intensity along the southern and central California coast. Accurate forecasting of blooms is challenging due to the numerous environmental factors that can influence algal growth. Marine chlorophyll-a concentration is one of the key indicators that can be used in monitoring and predicting algal blooms. Previous research efforts that used machine learning models to predict chlorophyll-a concentration in the southern to central California coastal region were mostly targeted at individual locations and used datasets covering fewer than eight years before 2019. In this study, wavelet analysis (WA) was used to pre-process chlorophyll-a marine long-duration time-series data to increase its suitability for machine learning by removing noise while retaining short-term spikes. SVR, Random Forest, XGBoost, ANN and LSTM machine learning models were then applied to the WA-integrated data pipeline along with water quality and meteorological inputs to predict chlorophyll-a concentration at three locations along the southern to central California coast. Additionally, datasets spanning from 2008 to 2025 were employed to address the shorter durations of the previous studies. The WA-ANN model achieved the overall best performance (Scripps Pier R^2 = 0.88, Cal Poly Pier R^2 = 0.79, Stearns Wharf R^2 = 0.75) for the three locations, accurately capturing the spikes indicative of algal blooms.
| 16 |
Author(s):
Ella Chan.
Page No : 133-140
|
Nuclear Fusion as a Future Energy Source: Safety, Challenges, and Prospects
Abstract
Nuclear fusion is a clean energy source which could help replace fossil fuels to minimize the harmful
global impact of climate change. A number of organizations globally have built nuclear fusion reactors,
but none of them are currently commercially viable. This review assesses nuclear fusion as a potential
energy source for the future, identifying key risks, advantages, and next steps. Hazards identified include
explosions, plasma instabilities, and magnetic discharge. Although these risks exist, the likelihood for
these accidents to occur was found to be minimal and appropriate safety measures are available. Key
challenges that emerged are limited fuel supply and high cost. The primary advantage of nuclear fusion
as an energy source is its efficiency (one gram of fuel can produce as much energy as ten tons of coal).
A review of historical progress, vital success metrics, and projected future development of nuclear fusion
indicates that nuclear fusion could be ready for commercial application as early as 2050. Future research
should focus on achieving ignition and self-sustaining plasma to reach commercial viability.
| 17 |
Author(s):
Haochen Sun.
Page No : 141-147
|
Algorithmic Classification of Music Emotion Based on Tempo and Tonality: A Comparative Analysis
Abstract
This study presents a quantitative algorithmic analysis to classify musical emotion by comparing
the predictive power of tempo and key. The work focuses on computational labeling rather than human
emotional perception or listener experience. Results show that both tempo and tonality contribute to
algorithm-based emotion classification, with tonality (key) yielding higher predictive accuracy than
tempo. Statistical testing confirms this difference is highly significant (p < 0.001). Major keys are
assigned positive algorithmic emotion scores, while minor keys receive negative scores, independent
of tempo. These findings offer a theoretical foundation for music psychology and potential implications
for music‑based interventions, including music therapy for children with autism. These results suggest
exploratory directions for future therapeutic design but do not constitute evidence of clinical efficacy.
This study provides an algorithmic baseline for future work and highlights the relative importance of
key and tempo in computational music emotion classification.
| 18 |
Author(s):
Zixiao Zhang.
Page No : 148-158
|
From Appropriation to Hybridity: A Comparative Study of Cultural Representation in American Dirt and Counting and Cracking
Abstract
This article comparatively examines the dynamics of cultural hybridization and cultural appropriation
through two contemporary case studies: American Dirt (2020) by Jeanine Cummins and the theatrical
production Counting and Cracking (2019) by S. Shakthidharan and Eamon Flack. While American Dirt
generated controversy for its portrayal of Mexican migrants by a non-Mexican author and was widely
criticized as cultural appropriation, Counting and Cracking exemplifies cultural hybridity through its
community-rooted and multilingual representation of the Sri Lankan diaspora. Drawing on theories of
transculturation, cultural appropriation, and hybridity, this article argues that the distinction between
hybridization and appropriation is shaped by historical power asymmetries and institutional frameworks.
It concludes that equitable cultural exchange depends on reciprocity, collaborative authorship, and
structural accountability within cultural industries.
| 19 |
Author(s):
Daud Tariq, Juliana Dellorco, Rayyan Shakkel.
Page No : 159-165
|
Ehlers-Danlos Syndrome: Using AI to Bridge the Diagnostic Gap
Abstract
Ehlers-Danlos syndrome (EDS) is a primarily genetic disorder, typically resulting from mutations
in collagen-encoding genes. There are thirteen varieties of EDS, each presenting different symptoms
and characteristics. Common symptoms include skin elasticity, hypermobility, abnormal scar formation,
and bruising. EDS is a complex syndrome and lacks a definite diagnosis method, leading to frequent
misdiagnosis, delayed treatment or management, and frequent feelings of resentment among patients.
While there is currently no definite diagnosis system, artificial intelligence (AI) is being evaluated to aid
in diagnosis, using methods such as AI-based video goniometry; Uniform Manifold Approximation and
Projection (UMAP), a dimensionality reduction technique that simplifies complex data while preserving
patterns; and Hierarchical Density-Based Spatial Clustering (HDBSCAN), an algorithm that utilizes
clustering, grouping similar data points together without requiring predefined categories. These methods
were then assessed on their feasibility, including key strengths and weaknesses such as availability
and accuracy. Overall, AI interventions in diagnostics are promising innovations that can act as potent
preliminary screening tools. However, the data sets that these models are trained on often lead to bias
and a lack of generalizability, causing unreliability in the readings. This is further compounded by the
fact that these models are “black boxes”, meaning that clinicians cannot access the underlying processing
route the machine uses to make a diagnosis. Therefore, while these models show promise, more clinical
studies are needed to prove their feasibility in the clinical landscape.
| 20 |
Author(s):
David Melamed, Konstantinos.
Page No : 166-176
|
Arthrogenic Muscle Inhibition After ACL Reconstruction: Implications for Return-to-Sport Decision-Making
Abstract
Anterior cruciate ligament (ACL) injuries are among the most common orthopedic injuries, with
approximately 250,000 cases occurring annually in the US. Surgical reconstruction (ACLR) followed
by extensive rehabilitation is the standard treatment for athletes returning to sport (RTS). Despite
the length and intensity of rehabilitation, between 20-40% of patients sustain a subsequent ACL tear.
This raises concerns regarding the adequacy of current RTS decision-making criteria. Arthrogenic
muscle inhibition (AMI), a neuromuscular impairment characterized by reduced voluntary activation
of the quadriceps following joint injury, has emerged as a potential contributor to persistent deficits
after ACLR. However, AMI is rarely directly assessed within RTS batteries. This systematic review
evaluated the role of AMI in RTS outcomes following ACLR. Of 3490 PubMed studies identified, 35
were included in the review. Literature consistently reports associations between AMI and reduced
quadriceps activation, neuromuscular asymmetries, altered gait mechanics, and kinesiophobia, all
factors associated with increased risk of reinjury. Yet, RTS protocols still rely primarily on time since
surgery, limb symmetry indices, and psychological readiness measures, with limited assessment of
underlying neuromuscular inhibition. These findings suggest that AMI may represent a contributing
neuromuscular factor influencing multiple domains used to determine RTS readiness. However, the
evidence is predominantly associative, and direct links between AMI and reinjury outcomes remain
limited. Future research should focus on developing reliable and feasible methods for assessing AMI and
evaluating whether incorporating such assessments into RTS batteries improves prediction of reinjury
risk and functional outcomes.
| 21 |
Author(s):
Anvi Jadhav.
Page No : 177-185
|
Comparing the Effects of Different Intergenerational Programs on Cognitive Function in Older Adults
Abstract
This review paper examines the impact of intergenerational activities between older adults and
younger individuals across community, institutional, home-based, and remote settings on the cognitive
health of older adults. It synthesizes findings from randomized controlled trials and observational
studies that have explored various types of intergenerational programs, including mentoring,
volunteering, companionship, and educational activities. Across studies, outcomes commonly include
mood, social connection, self-esteem, and cognitive performance in older adult participants. While
several studies report positive effects on cognitive and psychosocial outcomes, the evidence suggests
variation by program type and intensity. High-engagement models such as mentoring are associated
with improvements in executive function and related cognitive domains, whereas lower-intensity
support-based programs primarily affect psychosocial well-being with more limited cognitive change.
This review also highlights key characteristics of effective programs, including sustained engagement,
structured interaction, and cognitive challenge, as well as the importance of program design in shaping
outcomes. Despite promising findings, the literature is limited by small sample sizes, short intervention
durations, and inconsistent methodologies. Overall, the findings indicate a need for more rigorous, longterm
studies to clarify which types of intergenerational activities most effectively support cognitive
health in older adults.
| 22 |
Author(s):
Andrew Han, Eileen Pak, Kihyun Lee.
Page No : 186-190
|
Evaporation-Driven Electricity Generation Using Nanocarbon-Coated Fabrics
Abstract
The growing environmental impact of fossil fuel consumption highlights the need for renewable and
eco-friendly energy sources. In this study, we investigated evaporation-driven electricity generation
using fabrics coated with nanocarbon materials, carbon black (CB), multi-walled carbon nanotubes
(MWCNTs), and graphene nanoplatelets (GNPs). When a water droplet was introduced onto one side of
the coated fabric, capillary-driven ion transport through the nanocarbon coating generated a measurable
voltage. CB-coated fabric produced the highest and most sustained voltage, reaching a peak of 0.308
V at 100 s and remaining measurable up to 2000 s. MWCNT-coated fabric produced a peak voltage of
0.080 V with a response duration of approximately 500 s. GNP-coated and uncoated bare fabrics showed
negligible voltage responses over a 250 s measurement period. As a proof-of-concept demonstration,
four CB-coated fabric pieces were connected in series, producing a combined voltage of 2.252 V that
was sufficient to power a battery-free pocket calculator. These results indicate that CB-coated fabrics
have potential as a simple and low-cost platform for evaporation-driven electricity generation.
| 23 |
Author(s):
Yaoshan Jiang.
Page No : 191-198
|
A Study on the Relationship Between Respiratory Chest Expansion and Multi-Axis Body Motion Using a Wearable Belt Sensor
Abstract
This study investigates the relationship between respiratory chest expansion and multi-axis body
motion parameters measured using a wearable belt-like sensor equipped with an accelerometer and
gyroscope. A chest-mounted elastic belt sensor simultaneously recorded three-axis linear acceleration,
three-axis rotational velocity, and chest circumference displacement during a controlled breathing
exercise performed by a single healthy participant in a seated posture (n = 1,282 observations). Pearson
correlation analysis and multivariate linear regression were employed to quantify the associations among
these seven variables. Results indicate that linear acceleration components, particularly along the vertical
and lateral axes, exhibit moderate to strong correlations with chest expansion (r = 0.429 and r = −0.422
for the Y and Z axes, respectively, p < 0.001). Regression models for linear acceleration components
demonstrated substantially higher explanatory power (R² up to 0.697) compared to rotational components
(R² < 0.06), suggesting that translational body motion is more systematically coupled with respiratory
chest expansion than rotational motion. These findings contribute to the growing field of wearable
respiratory monitoring and demonstrate the feasibility of using low-cost inertial sensors to characterize
breathing biomechanics.
| 24 |
Author(s):
Athanasia Zervos.
Page No : 199-207
|
The Lifestyle and Environmental Drivers of Circadian Misalignment and Their Implications for Chronic Diseases
Abstract
The circadian rhythm is the body’s internal clock, regulating vital functions and systems such as
sleep cycles, hormone release, appetite, digestion, and temperature. Each organ is influenced by circadian
timing, while the master clock resides in the brain. The suprachiasmatic nucleus, a cluster of nerve cells
in the hypothalamus, coordinates biological processes in response to daylight and helps regulate the
master clock’s rhythm. Light signals from the eyes, combined with brain activity, mediate circadian
rhythms. However, daily life can disrupt these cycles. This paper reviews common yet detrimental
factors that influence the regulation of circadian rhythms. Diet, nutrient intake, and meal timing can
disrupt appetite and metabolism. Furthermore, studies have shown that stress, work shifts, screen usage,
and poor sleep can all lead to circadian misalignment. This misalignment is associated with chronic
health conditions such as obesity, sleep disorders, mental health disorders, heart issues, and cancer.
| 25 |
Author(s):
Michael Xu, Adem Tareen.
Page No : 208-219
|
Investigating the Effectiveness of High-Intensity Continuous Training in Trained Recreational Athletes
Abstract
High-Intensity Continuous Training (HICT), introduced by Joel Jamieson in Ultimate MMA
Conditioning, involves sustained slow-cadence, high-resistance repetitions for 10-20 minutes to improve
aerobic abilities such as fatigue resistance in Type II muscle fibers. Despite its proposed benefits for
athletes in sports which require repeated, near-maximal explosive efforts, HICT has yet to be scientifically
evaluated. This matched-subjects pilot study examined the effectiveness of a 10-week HICT intervention
on fatigue resistance in two 17-year-old recreational athletes (172.72 ± 2.54 cm, 140 ± 3 lbs), measured via
performance across Repeated Sprint Ability (RSA) and Cooper 12-minute run-walk tests. One subject
performed HICT twice weekly on a commercially available spin bike; the other served as a control.
Test analyses used Wilcoxon signed-rank and paired sample t-tests for between-subjects comparisons,
and Kendall’s tau correlations for within-subject performance trends. The experimental subject exhibited
a negative trend in RSA sprint times (τ = -0.764, p = 0.002), indicating improved fatigue resistance,
while the control showed a positive trend (τ = 0.556, p = 0.029), suggesting that HICT improved fatigue
resistance and recovery in Type II fibers. These effects may reflect enhanced phosphocreatine (PCr)
resynthesis, mitochondrial biogenesis, and lactate clearance. Cooper test results showed no significant
trends in either subject, leaving maximal oxygen uptake (VO2max) effects inconclusive. These limited
findings suggest HICT may be a viable technique for developing fatigue resistance in the fast-twitch
muscles of teenage athletes. Further research should incorporate larger samples, extended intervention
periods, formal measurement of external variables, and direct physiological measurements of aerobic
adaptations.
| 26 |
Author(s):
Advaita Ponduri.
Page No : 220-224
|
Nodding Syndrome in Uganda: The Detrimental Effect of Gaps in Healthcare, Biotoxins, Epigenetics, and Structural Violence
Abstract
This research paper is a literary review of the neurodegenerative disease known as Nodding
Syndrome which is a form of an epilepsy-related brain disorder found mainly in children from 5-15
years old. The main focus is on Uganda because Nodding Syndrome is most prominent in this area as
well as other third world countries in Africa. The primary objectives are to discuss how epigenetics
and structural violence, tied in with gaps in healthcare and exposure to biotoxins, interact to shape the
progression of this disease. This paper also examines the Ugandan healthcare structure, and the current
critical situation in the country regarding the Nodding Syndrome disease. All of this is tied together in
the paper to discuss how it affects vulnerable families in Uganda. After this research, this paper arrives
at the following major conclusions: First, these findings suggest that social conditions play a large role
in furthering the development of Nodding Syndrome. Second, the research highlights structural violence
as a main instigator of health disparities. Third, Nodding Syndrome has a lot of biomedical as well as
environmental factors that play into it through brain development and biotoxins. Finally, limited access
to healthcare and the lack of a well prepared medical system in Uganda makes diseases like Nodding
Syndrome much worse due to a multitude of factors such as inadequate treatment, a lack of supplies, and
poorly trained doctors.
| 27 |
Author(s):
Ruoxi Chen .
Page No : 225-232
|
Zebrafish as a Model Organism for Anti-Angiogenesis Cancer Drug Screening
Abstract
Angiogenesis is a critical biological process in development and disease, particularly in cancer
progression, where it supports tumor growth and metastasis. Vascular endothelial growth factor (VEGF)
signaling plays a central role in angiogenesis, making VEGF pathway inhibitors key therapeutic targets.
The zebrafish model provides a valuable platform for studying VEGF-driven angiogenesis and screening
anti-angiogenic compounds due to its optical transparency, rapid vascular development and genetic
similarity to humans. Several VEGF inhibitors, including SU5416 (Semaxanib) and PTK787 (Vatalanib),
have been widely used in zebrafish assays and consistently demonstrate dose-dependent inhibition of
intersegmental vessel formation. More selective inhibitors such as DMH4 show promising activity with
reduced off-target effects. Clinically approved or repurposed agents, including sorafenib and rosuvastatin,
exhibit variable inhibitory effects, while natural compounds such as baicalein offer potential lower
toxicity alternatives with modest efficacy. Overall, the zebrafish model provides a rapid, cost-effective,
and biologically relevant in vivo system for evaluating anti-angiogenic agents. This narrative review
summarizes current advances in zebrafish-based anti-angiogenic drug discovery, highlighting both
established inhibitors and emerging or repurposed compounds. Future studies should combine zebrafish
screening with molecular profiling and prioritize validation in mammalian systems to enhance clinical
translation.
| 28 |
Author(s):
Claire Shi.
Page No : 233-244
|
Green Roofs as a Pathway to Achieving Net-Zero Carbon in Construction
Abstract
The construction sector accounts for a substantial portion of global energy consumption and carbon
emissions, contributing to both embodied and operational carbon footprints. According to the 2015 Paris
Agreement, many countries worldwide are collaborating to achieve net-zero emissions by 2050. In the
construction industry, green roofs offer promising potential as a substantial structure for integrating into
buildings, helping to achieve net-zero emissions. This study aims to evaluate how green roof design
variables influence their effectiveness in achieving net-zero carbon goals within the construction sector.
A structured review of relevant scholarly and government literature was conducted to assess the features
and the relationship between green roofs and net-zero carbon. Findings indicate that substrate depth
(intensive or extensive) and vegetation properties (albedo, leaf area index, evapotranspiration, etc.)
contribute to the efficiency of green roofs in carbon mitigation toward net-zero in construction. This
review addresses a general knowledge gap in leveraging the natural carbon-eliminating properties (both
indirect and direct) of green roofs for net-zero carbon goals.
| 29 |
Author(s):
TAEHO KIM.
Page No : 245-254
|
Type Ia Supernovae Standardization Beyond Light-Curve Corrections: An In-Depth Investigation into the Mass-step Implementation
Abstract
Type Ia supernovae (SNe Ia) serve as standardizable candles crucial for probing cosmic expansion
and constraining cosmological parameters. SNe Ia have been standardized using their light-curves for
nearly three decades, but more recent studies in the past 15 years have found that SN Ia brightnesses
after light-curve corrections depend on their host-galaxy stellar masses. It has since become the standard
to correct for this unexplained phenomenon by applying a ‘mass-step’ correction, where SNe Ia in host
galaxies above a certain stellar-mass (mass-step location) are taken to be about 0.05 to 0.10 magnitudes
(mag) or about 5 to 10% brighter than SNe Ia in host galaxies below the mass-step location. In this paper,
three representative SN Ia surveys, the Dark Energy Survey 5 Year Supernova Analysis (DES-SN5YR),
Pantheon+, and the Joint Light-curve Analysis (JLA) are analyzed and compared against each other.
This work specifically focuses on the mass-step implementation in each of the surveys and how this
correction impacts inferred cosmological parameters across varying redshifts.
| 30 |
Author(s):
Syed Shah.
Page No : 255-260
|
Islamic Banking and Financial Stability: A Narrative Review of Risk-Reduction Mechanisms
Abstract
Debt-driven financial crises expose structural vulnerabilities in conventional interest-based banking
systems, yet alternatives based on ethical frameworks remain understudied in mainstream financial
literature. This narrative review examines how four core Shariah-compliant mechanisms—prohibition of
interest (riba, the Quranic term for unlawful excess), profit-and-loss sharing, asset-backed financing, and
ethical investment screening—reduce systemic financial risk. Drawing on published studies, institutional
reports, and case study evidence from the International Monetary Fund (IMF), major Islamic banks,
and regional financial authorities, this review demonstrates that Islamic banks experienced 25–40%
lower insolvency rates and required significantly less government intervention than conventional banks
during the 2008 global financial crisis. The findings suggest that Islamic banking’s faith-based structure
functions as an effective risk management framework by limiting leverage (debt-to-equity ratios
averaging 1:3 versus 1:10 in conventional banks), preventing speculative bubbles through mandatory
asset backing, and reducing exposure to volatile sectors through ethical screening. These results have
important implications for financial regulation and sustainable economic development, particularly in
contexts seeking alternatives to debt-dependent growth models.
| 31 |
Author(s):
Sherry Chen.
Page No : 261-272
|
Using Inferred Skin-Type Signals for Personalized Beauty Product Recommendation in a Hybrid System with Multi-Criteria Evaluation
Abstract
Skin type match is a key consideration of fit in beauty product recommendation, yet most deployed
recommendation systems do not have users’ skin type information readily available, and standard
precision-based metrics may not capture skin compatibility. This paper proposes a methodology to infer
customers’ skin type and integrates this signal into the hybrid beauty product recommender. Using
the Sephora dataset (8,494 products, 294,722 reviews), purpose-built skin-type signals were built—
including a skin-type-aware collaborative filtering matrix and a trained skin-type classifier—into
various hybrid skincare recommendation configurations. Eight domain-specific metrics are proposed to
evaluate the recommenders. A Bayesian weight optimization procedure using a Tree-structured Parzen
Estimator (TPE) was applied to find optimal weights for the hybrid system. The results yield four key
findings. First, personalization lifts are statistically significant across all four tested skin-type profiles
on skin compatibility and routine coherence. Second, no single configuration dominates on all metrics:
combining product content with skin profile achieves the highest skin-type compatibility and rank
sensitive precision among profile-aware variants; combining collaborative filtering with skin profile leads
on diversity and serendipity; full hybrid provides balanced performance across all metrics. Third, profile
weighting produces genuine inter-profile differentiation confirmed by positive adjusted personalization
scores across all hybrid variants. Fourth, Bayesian optimization identifies the skin-type classifier as
the dominant signal and reveals that enforcing a minimum content weight improves out-of-sample
generalization. These results confirm that these inferred skin-type signals and the use of a multi-criteria
evaluation framework can significantly improve the quality of the beauty product recommendation.
| 32 |
Author(s):
Anya Lu.
Page No : 273-280
|
Post-Pandemic Challenges and Resilience of Community-Based Businesses in Shanghai
Abstract
Although headline economic indicators suggested post-pandemic recovery, small businesses at the
street level continued to face ongoing challenges. This study examined community-based businesses
(CBBs) in Shanghai’s Meihua Road community, utilizing five rounds of storefront photography between
2022 and early 2026, and a questionnaire survey involving 12 shop managers in 2025. A three-dimensional
framework of location, business type, and daily operations was employed to assess the resilience and
policy needs of CBBs. Findings indicated that, despite macroeconomic recovery, the number of CBBs
dropped sharply in 2025, accompanied by rising vacancies and deteriorating commercial conditions. Key
hurdles included high rents, rising labor costs, and diminishing foot traffic. Consumption vouchers and
community-driven marketing campaigns demonstrated tangible advantages for CBBs. To support the
sustainability of CBBs, this study proposes targeted institutional relief from local governments, strategic
spatial governance by community authorities, and adaptive operational optimizations by business
operators.
| 33 |
Author(s):
Emily Han.
Page No : 281-298
|
The Climate Paradox of AI: A Historical Analysis of Academic, Industrial, and Public Narratives
Abstract
As AI capabilities have accelerated over the past decade, so have the questions surrounding their
environmental impact. This narrative review examines how perceptions of AI’s environmental impact
have evolved across academia, industry, and public discourse between 2014 and 2025. The focus is on
three key eras of development. Drawing on peer-reviewed literature, corporate sustainability reports,
and public publishing outlets, a qualitative Sentiment Concern Index (SCI) framework was used to
interpret shifts in optimism and concern across academia, industry, and publishing houses. The findings
suggest that while early academic and industrial discourse framed AI as a promising but untested tool,
more recent years have seen both increased deployment and growing criticism, especially regarding
the energy demands of large-scale models. Despite these concerns, the landscape is shifting toward
“green AI,” carbon-aware infrastructure, and environmentally responsible development practices.
The paper concludes with a forward-looking discussion of integrated strategies, emphasizing the
need for coordinated policy, technical innovation, public transparency, and cross-sector collaboration.
As AI becomes further embedded in society, ensuring that it functions as a climate asset and not an
environmental liability will be one of the defining sustainability challenges of the coming decade.
| 34 |
Author(s):
Jayden Shin.
Page No : 304-310
|
Modeling the Relationship Between Tax Revenue, Public Spending, and Economic Growth Across U.S. States
Abstract
This study examines the relationship between state tax revenue, public spending, and economic
growth across U.S. states using a quantitative financial-health framework. State-level data were obtained
from federal resources, including the U.S. Census Bureau’s Annual Survey of State Government Tax
Collections and Annual Survey of State Government Finances, and state gross domestic product (GDP)
data reported from the Bureau of Economic Analysis. Economic growth was measured as the annual
percentage change in real state GDP. Employing a linear regression model, the association between
fiscal variables and economic growth was assessed. The regression results showed that normalized tax
revenue and public spending explained a small portion of variation in 2021 state GDP growth, but neither
predictor was statistically significant. This suggests that the relationship between fiscal capacity, public
investment, and short-term economic growth is weak and should be interpreted cautiously. Although the
analysis does not establish causal relationships, the findings did not provide strong statistical support
for the proposed hypothesis. This study provides an accessible exploratory state-level analysis using
authoritative federal data and evaluates revenue and expenditure together as related indicators of fiscal
capacity and economic performance.
