| 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):
Hudson Le.
Page No : 299-303
|
A Narrative Review of Artificial Turf Vs. Natural Grass: The Safety of American Football Playing Fields
Abstract
This narrative review examines previously published literature investigating the association
between playing surface type and lower extremity injury rates in American football across high school,
collegiate, and professional levels. Injury rates reported in prior studies were descriptively compared
after standardization to injuries per 1,000 athlete exposures (AEs). No new primary data were collected
for this review. The results of the previously published literature indicate that the injury rates differ
depending on the level of play. In high school and collegiate levels, differences in injury rates between
turf and grass surfaces are inconsistent, while at the professional level, numerous studies indicate that
injury rates to the lower extremities are higher in synthetic turf surfaces compared to natural grass
surfaces.
| 35 |
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.
| 36 |
Author(s):
Aidan Lee.
Page No : 311-320
|
Sports-Related Spinal Cord Injuries in U.S. Youth Athletes: A Review of Recent Studies
Abstract
Sports-related spinal cord injury (SCI) remains a significant concern for youth athletes, particularly
in sports with a high risk of collisions or falls such as football, wrestling, gymnastics, and cheerleading.
This review examines recent literature on the epidemiology, on-field management, and acute care of
these injuries, along with updates on emerging treatment strategies. National registry data indicate that
youth and young adults face a disproportionately high risk of sports-related SCI despite a decline in
overall incidence. Early management guidelines emphasize rapid stabilization, airway protection, and
timely transport to appropriate medical facilities. Although current medical treatments primarily focus
on stabilization and rehabilitation, recent research in biomaterials, drug-delivery systems, electrical
stimulation, and cell-based therapies shows promise for enhancing neural repair. Most of these
approaches remain in preclinical stages, but ongoing advances in the field suggest the potential for more
effective interventions in the future.
| 37 |
Author(s):
Adaya Dong, Yixiao Shen.
Page No : 321-328
|
Recent Advance in Developing Activin-A Receptor Type I Inhibitors for Fibrodysplasia Ossificans Progressiva and Related Disorders
Abstract
Activin-A receptor type I (ALK2/ACVR1) is a central regulator of bone morphogenetic protein (BMP)
signaling, essential for development and tissue homeostasis. The recurrent R206H mutation drives
aberrant BMP pathway activation and underlies severe disorders including fibrodysplasia ossificans
progressiva (FOP) and diffuse intrinsic pontine glioma (DIPG), with distinct clinical outcomes depending
on cellular context. Current therapeutic strategies target mutant ALK2 through ligand-level blockade
such as anti–Activin-A antibodies, and direct kinase inhibition using small molecules ranging from early
dorsomorphin derivatives to next-generation candidates like BLU-782. Although these approaches have
improved potency, significant challenges persist. Emerging structure-guided and allosteric strategies
aimed at mutant-selective inhibition offer promising directions. Continued mechanistic and structural
characterization of ALK2 mutants will be critical for developing safe, selective and effective therapies.
This review aims to summarize recent advances in ALK2-targeted therapeutic strategies and to highlight
key challenges and future directions in the development of selective and clinically effective ALK2
inhibitors.
| 38 |
Author(s):
Anthony Wang.
Page No : 329-345
|
Single Cell RNA-Sequencing Reveals Metastatic and Therapeutic Signatures in Non-Small Cell Lung Cancer
Abstract
Lung cancer is the leading cause of cancer death globally, with non-small cell lung cancer (NSCLC)
accounting for most cases. This remains driven by its heterogeneity and metastatic potential. In this
research, the primary objective is to understand the differential expression of metastasis-associated
markers and differences in therapeutic potential, both of which are key to treatment outcomes. In this
study, cell scoring was applied to a dataset of 20 treatment-naive NSCLC patients, which consisted of
14 adenocarcinoma (ADC) patients, 3 squamous cell carcinoma (SCC) patients, 1 combined small cell
lung cancer (C-SCLC) patient, 1 patient with mixed adenocarcinoma and neuroendocrine carcinoma
(MANEC), and 1 pulmonary chondroid hamartoma patient undergoing surgical resections. This raw
dataset contained 9,001 cells and 24,873 genes. A narrative, literature-supported exploratory study
was conducted in which genes expressed in fewer than 3 cells and cells with fewer than 100 genes
were filtered out, resulting in 8,979 cells and 22,596 genes (Supplementary Figure S1). This allowed the
researcher to determine the relative proportions of immune and cancerous cells among the 8,979 cells
in total (Supplementary Figure S1). It enabled the researcher to assess the overall immune response in
patients diagnosed with NSCLC. Next, immune cells were filtered out to focus on cancerous epithelial
cells, resulting in a total of 3,324 cells. From these epithelial cells, known expression markers associated
with NSCLC subtyping, multidrug resistance, apoptosis resistance, and altered cancer metabolism
(GSE119911) were identified. In this analysis, the researcher demonstrated that the samples encompass
diverse immune cell populations that play a crucial role in shaping cancer heterogeneity. Concepts
of anti-apoptosis, multi-drug resistance, and metastatic potential are also explored using marker genes.
Cellular subtypes and clusters defined by the selected markers, which may otherwise be missed, were
characterized by the researcher using single-cell RNA sequencing. The observed results demonstrated
the utility of this method for uncovering cellular diversity, which could be used for cancer outcome
prediction and treatment decision-making.
| 39 |
Author(s):
Yechan Park, Jovin Desai, Mustafa Penwala.
Page No : 346-354
|
Design and Feasibility of a DIY Sink-Powered Micro-Hydroelectric Generator for Emergency Energy
Abstract
Reliable electricity is essential in modern households, yet power outages from extreme weather events
highlight the need for accessible, low-cost emergency energy solutions. This study investigates the
feasibility of a DIY sink-powered micro-hydroelectric system that passively harvests electrical energy
from routine household water use. A prototype 12V micro-turbine generator was tested under three
controlled flow conditions (low, medium, and high) across 17 total trials (5–6 per condition). Voltage
and current were measured using a digital multimeter, and instantaneous power was calculated as P =
IV. The system produced a peak instantaneous power of approximately 1 W under high-flow conditions,
with mean outputs of 0.06 W, 0.30 W, and 0.74 W for low, medium, and high flow respectively. Energy
output per gallon ranged from approximately 2 J at low flow to over 25 J at high flow. One-way ANOVA
confirmed statistically significant differences across conditions (F(2, 14) = 72.4, p < 0.001, η² = 0.91 for
power; F(2, 14) = 89.7, p < 0.001, η² = 0.93 for energy), with all pairwise comparisons significant by post
hoc Tukey HSD (p < 0.05). Extrapolating to conservative household faucet usage of 100 gallons per day,
the system could accumulate approximately 2,000 J daily and 203 Wh annually — sufficient to power a
2 W LED for roughly 100 hours or provide 10+ full smartphone charges per year. These findings support
the feasibility of tap-water-based micro-hydropower as a passive, accessible emergency energy solution.
| 40 |
Author(s):
Bhavana Thirunavukkarasan.
Page No : 355-363
|
Melanocortin-Driven Neuroprotection: Transcriptomic Evidence Supporting α-MSH Modulation as a Therapy in Parkinson’s Disease
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder marked by progressive loss of dopaminergic
neurons and chronic neuroinflammation. Current treatments improve symptoms but do not halt disease
progression. This study examines the anti-inflammatory and neuroprotective potential of α-melanocyte
stimulating hormone (α-MSH) in PD. Transcriptomic analysis of oxidation resistance 1 (OXR1)
activated, α-synuclein-overexpressing cells showed upregulation of melanocortin 1 receptor (MC1R) and
enrichment of pathways involved in apoptosis regulation, oxidative stress response, and inflammation
resolution, suggesting a shift toward cellular protection and immune modulation. Supporting murine
model data further suggest that MC1R activation is associated with reduced neuroinflammation and
improved neuronal survival, reinforcing melanocortin signaling as a potentially neuroprotective pathway.
α-MSH and its analogs may modulate microglial activation, mitochondrial function, and redox balance,
suggesting potential relevance to upstream pathological mechanisms beyond dopamine deficiency alone.
These findings highlight melanocortin signaling as a promising area for further investigation in PD,
though additional experimental and clinical validation is needed to determine its therapeutic applicability.
| 41 |
Author(s):
Ashley Lynn Forte.
Page No : 364-375
|
The Converging Contributors to Oral Squamous Cell Carcinoma: Environmental and Molecular Causes and Progressors
Abstract
Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for
tens of thousands of diagnoses per year in the United States. Despite various treatment options, the
5-year survival rate of OSCC patients in countries with integrated health systems is approximately
60% with high rates of recurrence and metastasis. This signifies the importance of understanding the
multifactorial etiology of OSCC incidence, progression, and mortality. For example, aberrant genetic and
epigenetic regulation of tumor suppressor genes in oral epithelial cells contributes to OSCC progression
through dysregulated cell proliferation. Tobacco and smoking, alcohol usage, betel nut consumption,
and direct contact with environmental pollutants are proven to significantly increase OSCC risk. Due
to the heterogeneity of disease, there are a multitude of treatment options available. They are based
on Tumor, Node, and Metastasis staging and grading and include independent or conjunctive therapies
of radiotherapy, immunotherapy, surgery, and photodynamic therapy. Additionally, clinical trials
are currently being completed to evaluate the efficacy of novel treatments such as gene therapy and
natural product-based interventions such as black raspberries. Collectively, this review aims to define
the molecular, histopathological, and clinical profiles of OSCC to provide novel insight into future
therapeutic modalities.
| 42 |
Author(s):
Rohan Madhok .
Page No : 376-384
|
Temporal Credit Assignment and Reward Granularity in a Songbird-Inspired Reinforcement Learning Model
Abstract
Sequential motor learning, such as precise imitation of birdsong syllables, depends on the brain’s
ability to link individual actions to delayed outcomes, a challenge known as the temporal credit
assignment problem. This problem arises because feedback often arrives only after a sequence unfolds,
obscuring which actions drive success or error. Inspired by songbird learning, this study investigates
how reward-feedback frequency (granularity) within a sequential vocal imitation task affects learning
efficiency in an actor-critic reinforcement learning agent. By systematically varying reward timing
while keeping cumulative feedback constant, results show that finely grained, step-by-step feedback
substantially accelerates learning and improves final imitation accuracy. In contrast, sparse feedback
(delivered only at midpoint or endpoint) substantially impedes learning. Even when training was extended
to 20,000 episodes, the end-only condition (K = 1) never reached the success criterion, and the half-only
condition (K = 2) reached it in only a small fraction of seeds. This indicates a continuous relationship
between feedback frequency and learning, rather than a sharp learnability cutoff. Notably, even under
the densest feedback condition (reward after every action), performance plateaued at a non-zero error.
Further analysis revealed that this plateau reflects not a single limit but two sources: the fixed exploration
noise in the policy, and a residual imitation error that persists once the noise is removed. These findings
characterize the trade-off between reward granularity and learning efficiency in a specific reinforcement
learning model and task. They also suggest directions for future investigation of reward scheduling in
biologically inspired learning systems.
| 43 |
Author(s):
Samarth Tewari.
Page No : 385-391
|
Structural Decoupling of Yield Curve Inversions and Equity Market Volatility in the Post-GFC Era: Evidence from Multi-Method Analysis with Structural Break Testing
Abstract
The current paper examines whether inversions in the yield curve of U.S. Treasuries continue to
possess the forecasting ability for equity market volatility in the post-Global Financial Crisis (GFC)
environment (2009-2024). With the use of 4,299 daily data points of the spread between 10-Year
and 2-Year Treasury rates (T10Y2Y) and the CBOE Volatility Index (VIX) obtained from the FRED
database, this paper uses Pearson correlation coefficient, Granger causality tests on daily (lags 1-5) and
monthly (lags 1-6) frequencies, ordinary least squares (OLS) regressions with macroeconomic controls,
Vector Autoregression (VAR), and Chow structural break test. Granger causality is not confirmed at all
tested horizons. OLS regressions explain less than 1% of the future VIX variance even after accounting
for S&P 500 returns. The Chow test confirms the presence of a statistically significant structural break
at the COVID-19 threshold (F = 214.32, p < 0.001). This means that there was a regime shift in the
relationship between the yield curve and the level of volatility. Results prove to be robust when excluding
the period of the coronavirus pandemic and winsorizing VIX at the 99th percentile.
| 44 |
Author(s):
Xiao Li, Jameson Augustin.
Page No : 392-399
|
Bitcoin Price Volatility, Mining Load Instability, and Demand Response Implications for U.S. Grid Operators
Abstract
The rapid growth of Bitcoin mining has created a new class of large, financially driven electricity
consumers whose demand responds to cryptocurrency markets rather than fixed production schedules,
raising concerns for grid planning and demand response design. This study examines whether Bitcoin
price volatility is associated with less stable mining incentives and whether mining-related instability
reduces the predictability of aggregate electricity demand. Using monthly data from January 2021
to November 2025 for Texas, the analysis combines Bitcoin price, hash price, electricity demand,
temperature, and heating degree day data in reduced-form ordinary least squares regressions. Bitcoin
price volatility is measured as the absolute monthly percentage change in Bitcoin prices. Mining
load instability is proxied by the absolute monthly percentage change in Bitcoin hash price, while
grid instability is measured using monthly variability in a state-level electricity demand proxy. The
results show that Bitcoin price volatility is significantly associated with mining load instability, with
a one percentage point increase in price volatility corresponding to an increase of approximately 2.5
units in the mining instability proxy. In contrast, mining load instability is not statistically significant
in explaining aggregate electricity demand variability at the monthly, system-wide planning horizon.
These findings suggest that mining-related risks may be masked at coarse time scales and require higherfrequency
operational data for direct reliability assessment.
| 45 |
Author(s):
Gaon Kim.
Page No : 400-409
|
The Influence of Socioeconomic and Environmental Factors on Allergic Sensitization: Population-Level Evidence From the 2005-2006 National Health and Nutrition Examination Surveys (NHANES)
Abstract
Allergic sensitization has been an increasingly important topic in public health policy as it can
influence individuals’ physical and mental well-being. In addition, it can potentially lead to fatal health
complications and symptoms. Existing studies have largely examined the influence of individual factors,
such as genetics or dietary habits, on allergic sensitization using laboratory data. However, limited
research has explored the socioeconomic and environmental factors that may trigger allergic reactions at
the population level. Thus, this study aims to identify which socioeconomic and environmental factors of
individuals may be associated with allergic sensitization using a large population-level survey: the 2005-
2006 National Health and Nutrition Examination Surveys (NHANES). It was hypothesized that higher
socioeconomic status (SES), better housing conditions, and the absence of a history of tobacco exposure
would be associated with reduced allergic sensitization. Taking the total serum Immunoglobulin E (IgE)
as an indicator of allergic sensitization, the regression analysis identified that the family Poverty Income
Ratio (PIR), the education level, and the number of people in the household had a strong association
with the total serum IgE. This indicated strong evidence for the significance of socioeconomic and
environmental conditions in allergic sensitization. These findings imply that living environments should
be an important consideration in public health policy, particularly for those with lower SES. They tend to
have higher allergic sensitivity, which can interfere with their daily functioning. This study contributes by
revealing a strong link between socioeconomic and environmental conditions and allergic sensitization,
with implications for public policy.
| 46 |
Author(s):
Aadhav Hariish.
Page No : 410-418
|
Machine Learning-Based Predictive Maintenance for Manufacturing: Optimizing Failure Detection with Sensor-Based Models
Abstract
Reducing unplanned equipment failures is critical for improving efficiency and lowering operational
costs in manufacturing systems. This study investigates the use of machine learning models to predict
machine failure using sensor-based operational data. The AI4I 2020 Predictive Maintenance Dataset
was used, which includes temperature, rotational speed, torque, tool wear, and machine type variables.
Exploratory data analysis identified key patterns, including nonlinear relationships between torque
and failure probability and strong correlations among certain sensor variables. Four machine learning
models, Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM),
were developed and evaluated. Due to class imbalance, performance was assessed using precision, recall,
and F1-score, with emphasis on failure detection. While most models achieved high accuracy (0.97-0.99),
Logistic Regression performed poorly in identifying failures (recall = 0.12). SVM achieved a high recall
(0.84) but suffered from low precision (0.27), resulting in excessive false positives. A tuned Gradient
Boosting model with threshold optimization achieved the best overall performance, with a precision of
0.77, a recall of 0.74, and an F1-score of 0.75. Receiver operating characteristic (ROC) analysis showed
strong model discrimination, with an area under the curve (AUC) of 0.96 for Gradient Boosting and
0.95 for Random Forest. Compared to the untuned model, recall improved substantially, enabling the
detection of more failure events with a moderate increase in false positives. These results demonstrate
that ensemble methods, combined with threshold tuning, provide an effective approach for predictive
maintenance by balancing failure detection and false alarm rates.
| 47 |
Author(s):
Rishabh Kamani.
Page No : 419-429
|
Electrochemical Hydrogen Compression: Thermodynamic Advantages and Practical Constraints Relative to Mechanical Systems
Abstract
The growing limitations of fossil fuel dependence have accelerated interest in hydrogen as a clean
energy carrier. However, hydrogen’s low volumetric energy density necessitates compression, introducing
thermodynamic inefficiencies and material challenges that impact system performance. Mechanical
hydrogen compressors currently dominate due to technological maturity and scalability, but operate
under non-ideal conditions characterised by near-adiabatic behaviour, frictional losses, and material
degradation. Electrochemical hydrogen compression presents an alternative approach, offering nearisothermal
operation, reduced mechanical losses, and integrated purification. Despite these theoretical
advantages, its performance is constrained at higher pressures by hydrogen crossover, membrane
limitations, and accumulating electrochemical irreversibility. This review evaluates mechanical and
electrochemical hydrogen compression within a unified thermodynamic framework, linking performance
limitations to their underlying physical mechanisms. The analysis shows that electrochemical compression
offers higher efficiency at low-to-moderate pressures, while mechanical compression remains more
practical for high-pressure, large-scale applications. Furthermore, the findings highlight key trade-offs
and identify critical research directions required to improve electrochemical compressor performance
and enable broader adoption within future hydrogen energy systems.
| 48 |
Author(s):
Hailey Haein Yoon.
Page No : 430-440
|
Leveraging Computational Tools to Enhance Small-Molecule Drug Design for Cystic Fibrosis and Beyond
Abstract
Small-molecule drugs, organic compounds that interact with proteins and enzymes, represent a
powerful approach for treating genetic diseases. These drugs are designed to alter specific molecular
disease targets and do so with reduced off-target effects. Furthermore, these drugs offer ease in
consumption through oral delivery. New technology, especially computational tools, have enabled the
enhanced modeling of design of small-molecule drugs. These advancements are particularly effective
in the treatment of genetic disease, where targeting mutated proteins can restore function and reduce
disease progression. This project uses SwissDock molecular docking to assess how modifying chemical
structures of the cystic fibrosis small-molecular drug Ivacaftor can enhance the drug’s interaction with
the mutated CFTR protein. Among the 4 modified Ivacaftor analogs tested alongside the unmodulated
structure, hydroxyl-group-deleted Ivacaftor was shown to have the lowest average SwissParam score and
thus the highest binding affinity to the mutated CFTR protein. The broader implications of this work
highlight how structure-based drug design can lead to the development of more effective treatments for
genetic disease and be applied for the treatment of other conditions, ultimately expanding the scope of
precision medicine.
| 49 |
Author(s):
Woosung Choi, Wang Kai Y.
Page No : 441-450
|
Visible-Light Driven Ag-TiO2 Photocatalyst Prepared by Simple Physical Mixing of Commercial Colloidal Silver and TiO2 Suspensions for Indo or VOC Degradation
Abstract
Sick Building Syndrome arises from volatile organic compounds (VOCs) emitted indoors by
household materials. Photocatalytic oxidation with TiO2 has been studied to reduce indoor VOCs, but
TiO2 mainly absorbs ultraviolet light. Indoor lighting is dominated by visible light, so the use of TiO2
indoors becomes limited. In previous studies, AgNP was added on TiO2 to push its activation range into
visible light, mostly by sol-gel methods. This research examined whether simply mixing two commercial
products, a colloidal silver solution and a TiO2, can produce a visible-light driven AgNP-TiO2 without
chemical synthesis. The key question was whether visible-light activation through SPR sensitization
requires direct Ag-O-Ti chemical bonding, or whether physical proximity between AgNP and TiO2 in
the dried coating is sufficient. 1 wt% and 5 wt% AgNP-TiO2 were prepared by physical mixing and
characterized by UV-Vis spectrophotometry and X-ray diffraction (XRD). SPR peak was observed at
around 430 nm. XRD confirmed the anatase phase of TiO2 with rutile, and no peak shift was observed
after AgNP addition. The samples were sprayed on paint-coated containers, and VOC removal was
measured under UV and fluorescent lamps. VOC removal under fluorescent light followed the order of 5
wt% > 1 wt% > pure TiO2. The visible-light activity in the absence of a chemical synthesis step can be
understood from the photon-based nature of the SPR effect. AgNP absorbs visible photons by plasmonic
oscillation of surface electrons, and the energy is transferred to TiO2 via near-field coupling. Such a
process does not require Ag-O-Ti bonding.
| 50 |
Author(s):
Shamsiddinov Nodir.
Page No : 451-460
|
Systematic Analysis of Noise Injection Strategies for Robust Visual Localisation: Matching Synthetic Noise to Real-World Sensor Degradation Patterns
Abstract
Visual odometry systems, technologies that help machines understand their position by analysing
camera images, are essential for safety-critical applications like autonomous vehicles and robotic
navigation. These systems work well in controlled laboratory conditions, but they often struggle in realworld
deployment, where cameras face challenges such as sensor readout noise and motion blur. The key
problem is that training data used to teach these systems is often too clean and simplified, leaving them
unprepared for challenging real-world conditions. This study demonstrates that the systematic injection
of realistic image corruptions during training reduces median translation error by 20–70% relative to
a non-augmented baseline, substantially improving model robustness across a range of degradation
scenarios. Five types of synthetic noise were evaluated—Gaussian noise, Poisson noise, motion blur,
spatially correlated noise, and combinations of these—comparing models trained on clean data against
those trained with augmented data, all using an optimization approach called Conservative Pose Loss.
Noise-augmented models consistently achieved lower position and orientation errors than the baseline,
and this advantage was maintained even when encountering corruption types not seen during training.
These results demonstrate that incorporating realistic noise patterns into training data is crucial for
deploying reliable visual odometry systems in safety-critical real-world applications. These findings
suggest that careful attention to data augmentation strategies can bridge the gap between laboratory
performance and practical deployment, potentially improving the safety and reliability of autonomous
systems navigating complex environments.
| 51 |
Author(s):
Olivia I. Thompson.
Page No : 461-467
|
Beyond Treatment: Confronting Cancer at Its Roots
Abstract
Cancer remains one of the most complex and persistent diseases in modern medicine, claiming millions
of lives each year despite remarkable advances in treatment. While therapies such as chemotherapy,
radiation, surgery, and immunotherapy have improved survival and extended life for many patients,
they frequently fail to provide a permanent cure and often carry substantial physical, emotional, and
economic costs. Increasingly, researchers and public health experts argue that treatment alone cannot
sufficiently reduce the global cancer burden. In this perspective article, I examine the growing body
of literature suggesting that greater emphasis should be placed on cancer prevention by addressing
environmental and lifestyle risk factors that contribute to disease development. Drawing on insights from
cancer biology, epidemiology, and public health research, I explore the biological complexity that makes
cancer difficult to cure, the limitations of treatment-centered approaches, and the powerful impact of
prevention strategies such as smoking cessation, vaccination, and screening. Evidence from major public
health interventions demonstrates that reductions in carcinogenic exposures have already prevented
millions of deaths. These findings suggest that preventing cancer before it begins may offer the most
effective long-term strategy for reducing human suffering and alleviating strain on healthcare systems.
Ultimately, I argue that while treatment will always remain essential, shifting research priorities toward
prevention, particularly through federal funding agencies such as the National Institutes of Health, could
substantially reduce cancer incidence and reshape the future of cancer control.
| 52 |
Author(s):
Advay Arashanapalli.
Page No : 468-479
|
Feature Level Impacts of TTS and STT on Literacy Outcomes in Students with Dyslexia
Abstract
This study examines how assistive technologies, specifically text-to-speech (TTS) and speech-to-text
(STT), influence literacy outcomes in students with dyslexia. A sequential explanatory mixed methods
design combined a systematic literature review with descriptive feature-level coding of published
intervention studies and qualitative interviews with experienced educators. The quantitative phase
coded 16 published studies involving elementary through high school students who used TTS, STT, or
related assistive technology tools. Studies were analyzed by tool type, literacy outcome, intervention
duration, major features, reported outcome direction, and other key metrics. The qualitative phase
included semi-structured interviews with three teachers experienced in supporting students with
reading disabilities, with responses coded for tool use, perceived benefits, implementation conditions,
and barriers. Across the coded literature, TTS appeared most frequently and was commonly associated
with improved reading comprehension and fluency while STT was more often linked to increased
writing productivity and reduced spelling barriers. Features such as synchronized highlighting, pacing
control, and error correction appeared repeatedly across studies reporting positive outcomes. The
teacher interviews reflected similar patterns, with educators describing increased student confidence,
stronger task completion, and improved access to grade level content when assistive technologies were
implemented. Reported barriers include limited device access, inconsistent training, and concerns about
overreliance on technology. These findings suggest that assistive technology can improve literacy access
and performance when paired with targeted instruction and supportive implementation conditions.
| 53 |
Author(s):
Ellie Yang.
Page No : 480-491
|
Curcumin as A Dual Treatment for Alzheimer’s Disease
Abstract
Alzheimer’s Disease (AD) is the most common form of dementia, hallmarked by amyloid-beta (Aβ)
plaques, tau tangles, and neuroinflammation. Available therapeutic options have shown only marginal
symptomatic relief and have not shown efficacy in halting the progression of the disease, calling for
additional therapeutic options. Recently, Curcumin, a bioactive polyphenol extracted from the rhizome
of Curcuma longa or turmeric, has gained popularity for its potential neuroprotective activity. However,
Curcumin has its own disadvantages, making it important to evaluate synergistic nutraceutical approaches
designed to overcome Curcumin’s barriers. Combining Curcumin with other compounds has been
shown to compensate for Curcumin’s disadvantages and enhance the efficacy of Curcumin treatment.
For example, combining Curcumin with Piperine, Berberine, Resveratrol, and Vitamin D3 has been
shown to enhance the bioavailability of Curcumin treatment while helping reduce neuroinflammation,
improve memory, or improving the Blood Brain Barrier (BBB). This review explores the combination
of Curcumin and several different compounds and found that the Curcumin Piperine combination
remains the most promising candidate for clinical standardization due to its direct role in preventing Aβ
aggregation. However, current literature is limited by a reliance on in vitro and animal models, alongside
inconsistent cognitive outcomes in existing human trials. While these natural synergies still need further
research, they represent promising low-toxicity alternatives to traditional pharmaceuticals, but further
well-designed, long-term clinical research is essential to establishing them as viable therapeutic options
for AD patients.
| 54 |
Author(s):
Vivan Patel.
Page No : 492-505
|
Reversing AT2 Cell Senescence in Idiopathic Pulmonary Fibrosis via CRISPRi-Mediated Serpine1 ( PAI-1) Modulation
Abstract
Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with a median survival
of 2.5 to 3.5 years post-diagnosis. Current FDA-approved antifibrotic therapies, including pirfenidone
and nintedanib, slow disease progression, yet fail to address the underlying cellular dysfunction
driving fibrosis. A key factor driving IPF pathogenesis is the senescence of alveolar type 2 (AT2) cells,
the progenitor cell population that maintains and repairs the alveolar epithelium. When AT2 cells
undergo the Senescence-Associated Secretory Phenotype (SASP), AT2 cells lose their regenerative
capacity and secrete pro-inflammatory and pro-fibrotic factors that contribute to scarring of the lung
tissue, or fibrosis. SERPINE1, the gene encoding Plasminogen Activator Inhibitor-1 (PAI-1), which is
significantly upregulated in AT2 cells of IPF patients, has been mechanistically linked to senescence
through inhibition of p53 degradation, activating the p53-p21-Rb cell cycle arrest pathway. Conditional
knockout studies in murine models suggest that AT2-specific PAI-1 regulation suppresses senescence
and weakens IPF progression, establishing SERPINE1 as a causal driver rather than a passive biomarker.
This proposal outlines a gene therapy that utilizes an adeno-associated virus (AAV) vector to deliver
a CRISPR interference (CRISPRi) system composed of dCas9 and a KRAB repressor domain. This
system would downregulate SERPINE1 transcription without the creation of double-strand DNA breaks,
offering a safer alternative to conventional CRISPR-Cas9 therapies. By targeting the upstream driver of
AT2 senescence, this approach has the potential to restore alveolar regenerative capacity, reverse disease
progression, and meaningfully improve IPF patient outcomes.
| 55 |
Author(s):
Katie Chung.
Page No : 506-517
|
AI Data Center Expansion and Its Implications for Energy Demand and Environmental Justice: Evidence from New Jersey and Comparative U.S. Regions
Abstract
The rapid growth of Artificial Intelligence (AI) has sharply increased demand for data center
infrastructure, raising concerns about energy use, greenhouse gas emissions, and environmental justice.
This study examines AI-driven data center development in New Jersey, estimating effects on electricity
demand, air pollution, and public health using a transparent screening framework. Facility-specific
capacity data are compiled from publicly accessible sources, EPA eGRID 2022 average emission rates
are applied under two utilization scenarios, public health effects are assessed using the EPA COBRA
screening model, and environmental justice implications are evaluated using EPA EJScreen and NJDEP
overburdened-community mapping tools. Screening results indicate that New Jersey’s disclosed data
center capacity could expand 4.16-fold, increasing estimated electricity demand from approximately
2.06–2.74 TWh per year (2.8–3.7% of statewide retail sales) to 8.55–11.40 TWh per year (11.6–15.5%
of statewide retail sales). Associated CO₂ emissions under full build-out are estimated at approximately
2.55–3.40 million metric tons per year, with modeled health damages reaching approximately $271–$450
million annually under high utilization. Several existing and planned facilities are located in or near
communities already identified as socioeconomically vulnerable or facing elevated pollution burdens.
Comparative analysis with Northern Virginia and Central Ohio illustrates how rapid, large-scale data
center growth creates grid reliability, cost allocation, and environmental equity challenges in the absence
of proactive policy frameworks. These findings support improved facility-level disclosure, targeted tariff
design, cleaner incremental electricity supply, and rigorous tract-level environmental justice review.
| 56 |
Author(s):
Coral Bhattacharjee.
Page No : 518-525
|
Causes of Higher Cortisol Levels in Astronauts During Space Missions and Effects of Implemented Stress Reduction Measures
Abstract
Mental health challenges among astronauts have become a critical concern as space agencies prepare
for longer missions to the Moon and Mars. Although spaceflight technology has advanced to support
physical survival in microgravity, the psychological effects of isolation, confinement, and sensory
deprivation remain major obstacles to human performance and mission success. This paper reviews
published literature on mental health decline during extended space missions, focusing on environmental,
physiological, and interpersonal factors. Drawing on NASA behavioral health reports, analog
environment studies, and literature on cortisol as a stress biomarker, this review synthesizes evidence
linking chronic physiological stress with emotional and cognitive deterioration. The reviewed literature
suggests that astronauts may experience elevated cortisol levels, poor sleep quality, and increased mood
disturbances over time. Elevated cortisol and sleep disruption are frequently discussed alongside reports
of anxiety, irritability, and emotional detachment, particularly during the midpoint and later stages of
missions. Disrupted circadian rhythms, sensory monotony, and interpersonal conflict emerge as major
contributors to psychological strain. The literature also highlights important research gaps, including
small sample sizes, reliance on self-reported data, and insufficient study durations to accurately model
deep-space missions. The synthesized evidence indicates that current countermeasures, including
structured communication and relaxation protocols, may be insufficient for maintaining mental health
during multi-year expeditions. Potential future interventions include improved environmental design,
real-time biomarker monitoring, expanded use of virtual reality therapy, and enhanced psychosocial
training. Protecting astronaut mental wellbeing is therefore essential not only for individual health but
also for safe and successful long-duration space exploration.
| 57 |
Author(s):
Coral Bhattacharjee.
Page No : 526-530
|
Music Therapy for Mental Health Problems: Review and Proposal
Abstract
This narrative review examines current evidence on music therapy for depression, anxiety disorders,
schizophrenia spectrum disorders, and related mental health conditions. Across the reviewed literature,
both receptive and active music therapy approaches appear promising as adjuncts to standard care, with
the strongest support seen in short-term improvements in depressive symptoms, anxiety symptoms,
negative symptoms in schizophrenia, and selected quality-of-life outcomes. At the same time, the
literature remains limited by heterogeneous protocols, uneven reporting of personalization strategies,
inconsistent measurement of mechanisms such as stress regulation and adherence, and sparse longterm
follow-up. Based on these recurring gaps, this paper also outlines a conceptual future study design
centered on a closed loop personalized music therapy model that combines therapist-led sessions with
adaptive home practice informed by symptom check-ins and heart rate variability. Overall, the reviewed
evidence supports music therapy as a credible low-risk adjunctive approach while highlighting the need
for more standardized and personalized future research.
| 58 |
Author(s):
Aaryan Verma, Anvitha Kakarlapudi, Tamiika Hurst-Darby, PhD.
Page No : 531-540
|
A Multimodal Approach To Pain Detection Using Facial Action Units and Convolutional Neural Networks
Abstract
Optimal chronic pain detection requires objective and timely communication. However, patients
are often unable to convey the intensity and length of pain, leading to frequent miscommunication in
clinical settings. To address this issue, a machine learning model was developed to detect chronic pain
in a timely, less subjective manner. A manually labeled facial image dataset was used for formal model
training and held-out testing. Separately, locally collected volunteer images, including a consented
image used as an illustrative example, were used only for exploratory demonstration. Three versions
were applied, an Action Unit (AU) based model, a Convolutional Neural Network (CNN), and a hybrid
model that consists of both AU and CNN features. The hybrid model achieved a testing accuracy of 0.91,
outperforming the individual models. Though the small sample size and limited formal chronic pain
representation imposed restrictions, this study demonstrates the possibility for AI-based pain detection
to be a reliable, on-demand method that can be used alongside clinical examination to improve pain
assessment for patients with limited communication.
| 59 |
Author(s):
Arnav Daga.
Page No : 541-548
|
Evaluating Extendable Turbulators for Airfoil Performance Optimization Across Flight Regimes Using Computational Fluid Dynamics
Abstract
Commercial aviation faces ongoing pressure to improve fuel efficiency. Turbulators, which are small
dimple or fin-like devices mounted on aircraft wings, are known to delay flow separation and reduce
stall risk by energizing the boundary layer at high angles of attack and low air speeds. However, at cruise
altitude, where the Reynolds number is naturally high and the airflow is already turbulent, turbulators
provide negligible aerodynamic benefits while increasing drag and reducing fuel efficiency. This
study investigates whether extendable turbulators, which are specifically meant to deploy only during
specific phases of flight, could improve aerodynamic efficiency and potentially reduce fuel consumption
in commercial aviation. Using two-dimensional Computational Fluid Dynamics (CFD) simulations
conducted in ANSYS Fluent, four NACA airfoil geometries (NACA 0012, 2412, 4412, and 6412) were
evaluated under two flight conditions: takeoff (12.5° angle of attack, 463 kph) and cruise (5° angle of
attack, 850 kph), each with and without a leading-edge turbulator. Results demonstrate that at cruise
conditions, turbulators increased drag by an average of 83% while providing negligible lift improvement,
substantially reducing aerodynamic efficiency. During high-angle-of-attack conditions, turbulators
increased drag while producing flow features consistent with boundary-layer energization and delayed
separation. These findings support the feasibility of extendable turbulators as a practical design solution
to preserve stall safety benefits while eliminating the drag penalty during cruise flight.
| 60 |
Author(s):
Jihun Jung.
Page No : 549-556
|
Simulation-Based Modeling of Predictors of Hypertension Risk Among Young Adults in the United States
Abstract
Hypertension risk is an important concern in the area of public health since increased blood pressure
elevates the risk of cardiovascular disease, stroke, and other long-term complications. However,
it is often under-recognized in young adults. In this study, a simulation-based quantitative modeling
approach was taken to examine predictors of hypertension among young adults in the United States. A
total of 2,000 individuals aged from 18 to 39 were generated by simulation grounded by prior literature
to reflect plausible demographic, behavioral, anthropometric, and metabolic patterns reported in prior
epidemiological research in the United States. Variables included age, sex, race/ethnicity, body mass
index (BMI), smoking status, alcohol use, physical activity, fasting glucose, total cholesterol, and
hypertension status. The simulated sample was summarized by descriptive statistics, and binary logistic
regression was used to estimate the independent correlations between aforementioned predictors and
hypertension. Hypertension prevalence in the simulated sample was reported to be around 11.5%. Higher
age within the young adult range, higher BMI, smoking, increased glucose, male sex, and Black race
were positively correlated with greater odds of hypertension. Physical activity indicated a statistically
significant protective association with hypertension odds, with participants meeting guidelines for
physical activity having lower modeled odds of hypertension. Among the modeled predictors, BMI
was reported to be the strongest correlations with hypertension risk. The model indicated acceptable
discriminatory performance, and the area under the receiver operating characteristic curve was around
0.77. Overall, the findings in this study suggest that hypertension risk in young adults is shaped by
multiple demographic, behavioral, and metabolic factors, emphasizing the value of simulation-based
modeling in public health research.
| 61 |
Author(s):
Shaunak Thamke.
Page No : 557-567
|
Traffic Control System: Optimization of Signal Efficiency Using Deep Reinforcement Learning
Abstract
Traffic congestion remains a major challenge in modern cities. It contributes to delays, fuel waste,
pollution, and reduced quality of life. Most intersections still rely on fixed-time signals that do not adapt
to real-time traffic demand. This research investigates whether Deep Reinforcement Learning (DRL) can
optimize signal timing more effectively than traditional approaches. Historical Kaggle traffic data and
real-time New York City (NYC) data was used to generate Poisson-based vehicle arrivals across fourway
intersections. Two DRL agents, Proximal Policy Optimization (PPO) and Deep Q Network (DQN),
were trained to minimize vehicle wait-time (delays) and queue length (congestion). Although DQN has
been widely used in previous traffic-signal studies, PPO remains underexplored. This work provides
an evaluation of both methods against two baselines: Fixed-Time Baseline Controller (FBC) that used
Kaggle data and Analytical Baseline Controller (ABC) that used real-time NYC data. PPO performed
the best and reduced average wait-time by 90.9% compared to FBC and 69.46% compared to ABC.
DQN saved 61.32% compared to ABC. PPO consistently delivered greater reductions and stability across
runs. Generated heatmaps based on 20 simulations confirmed the adaptability of PPO in maintaining
low queue lengths and avoiding severe congestion, common in fixed-time and analytical systems. The
DQN heatmap showed reduced queues with occasional variability, while PPO was stable. These findings
highlighted the potential of PPO for dynamic, data-driven traffic control which was demonstrated using
interactive traffic simulation. This research has great potential in improving traffic flow efficiency,
reducing congestion and pollution.
| 62 |
Author(s):
Jayden Cho.
Page No : 568-591
|
Structure-Based Comparative Docking Analysis of Fungal Metabolites Against NDM-Family Metallo-β-Lactamases
Abstract
Carbapenemase-producing bacteria are a major global health concern because they can hydrolyze a
broad range of β-lactam antibiotics, including carbapenems that are often used as last-resort treatments.
Among these enzymes, New Delhi metallo-β-lactamases (NDMs) are particularly problematic due to
their broad substrate spectrum, rapid global dissemination, and the limited availability of clinically
effective inhibitors. In this study, a structure-based docking analysis of selected fungal metabolites
against NDM-family metallo-β-lactamases was performed. Seventeen fungal metabolites were initially
screened, and eight compounds were selected for docking against eight experimentally resolved NDMfamily
structures using CB-Dock2. The resulting complexes were further examined and visualized using
UCSF Chimera. Protein-ligand interactions were analyzed using PLIP and electrostatic surface mapping.
An empirical cutoff (Vina score ≤ −8.0 kcal/mol) was used as a practical prioritization criterion rather
than as a predictor of inhibitory activity. Pulvinic acid, Pseurotin A, Hispidin, and Scytalone were
prioritized for further analysis. Hispidin and Scytalone tended to localize near catalytic regions, whereas
Pulvinic acid and Pseurotin A more frequently occupied adjacent or substrate-entry regions. Reference
β-lactamase inhibitors were included to provide structural context for docking interpretation. These
ligands localized within predicted binding regions and provided a basis for comparison with fungal
metabolites. None of the compounds demonstrated direct Zn²⁺ coordination, suggesting that classical
zinc-chelation-based inhibition is unlikely. ADMET predictions indicated that Hispidin and Scytalone
possess relatively favorable drug-like properties, whereas Pulvinic acid and Pseurotin A may serve
as initial scaffolds for further optimization. Overall, this study highlights fungal natural products as
structurally diverse compounds that may serve as useful starting points for exploring ligand interactions
with NDM-family metallo-β-lactamases and generating hypotheses for future inhibitor development.
| 63 |
Author(s):
Jacob H. Boms.
Page No : 603-610
|
Applications of Prospect Theory to Electoral Behavior
Abstract
Prospect Theory is a descriptive model of behaviour under varying conditions of uncertainty, linking
an individual’s risk preference to their reference point and to other behavioural patterns associated
with risk. Although Prospect Theory was initially developed as a model of individual decision making,
Prospect Theory has also been applied to organizational contexts, making it relevant to the study of
electoral behaviour. The theory has been used to examine several areas in electoral behaviour, such
as vote buying, party strategies, voter participation, leader’s strategies and reform requirements, often
complementing existing electoral models. Prospect Theory concepts such as loss-aversion and reference
dependence recur across this literature, particularly in discussions of voter preferences and responses to
risk. This paper first presents an overview of Prospect Theory alongside relevant advancements to the
theory. It then organizes applications of Prospect Theory to electoral behaviour into thematic sections,
while situating these applications within broader electoral behaviour research. This review concludes
by highlighting areas where further empirical research may be beneficial to understanding Prospect
Theory’s explanatory value in electoral behaviour.
| 64 |
Author(s):
Dawon Kim.
Page No : 611-618
|
A First-Order Mathematical Model of Peanut Tolerance Progression during Oral Immunotherapy
Abstract
Peanut allergy is potentially a life-threatening condition that places significant clinical and
psychological burden on affected children and their families. In spite of the strong efficacy of peanut
oral immunotherapy (OIT) shown in increasing tolerance thresholds, the time-dependent progression of
desensitization has not been much expressed through a simple quantitative framework. In this study, a
first-order dynamic quantitative model has been developed to describe the progression of peanut tolerance
during OIT using published aggregate clinical outcomes from the U.S. Food and Drug Administration
(FDA) statistical review of Phase 3 Study ARC005. In the referenced trial, 73.5% of AR101-treated
participants tolerated at least 600mg of peanut protein at the exit double-blind, placebo-controlled food
challenge, where only 6.3% in the placebo group reported it after approximately 12 months of therapy.
Assuming that the rate of improvement was proportional to the remaining proportion of participants
who had not yet achieved the tolerance endpoint, a first-order differential equation was used to represent
the increase in tolerance probability over time. With the fitted model, a desensitization rate constant of
approximately 0.111 month-1 and a tolerance half-time of about 6.26 months were produced. The results
suggest that a simple first-order mathematical model can provide an interpretable representation of
peanut desensitization dynamics during OIT in a population level and also serve as a useful conceptual
framework for future modeling studies.
| 65 |
Author(s):
Sakura Ozaki, Yusuke Nagai.
Page No : 619-626
|
Implementing Japanese Origami in STEAM Education: A Report From an International Astronomical Youth Camp
Abstract
This study examined the potential of Japanese origami for STEAM education through questionnaires
and two 90-minute hands-on origami workshop sessions conducted at the International Astronomical
Youth Camp (IAYC), with each participant attending one session. In the pre-camp survey (N=45),
76% reported prior origami experience, and overall interest was high, although many participants
were uncertain about its educational usefulness in astronomy. Among the 19 workshop participants
who completed the post-workshop survey, 74% reported being very satisfied with the workshop, and
“concentration” was the most frequently identified cultivated skill. Among the 18 participants whose
pre-camp and post-workshop responses were successfully matched, both interest in origami (p<0.001)
and perceived educational usefulness in astronomy (p=0.002) increased significantly after the workshop.
Free-text responses frequently included engineering-related terminology such as “deployment” and
“folding,” suggesting a strong conceptual connection between origami and space-related contexts.
Despite limitations including a small sample size and short intervention duration, the findings indicate
that origami can enhance motivation and educational understanding in multicultural and time-constrained
learning environments. These results support the integration of origami into interdisciplinary STEAM
education, particularly in astronomy-related contexts.
| 66 |
Author(s):
Tallulah Echtenkamp.
Page No : 636-645
|
A Systematic Metareview of the Impact of Social Media Use on Adolescent Mental Health
Abstract
This paper analyzes 32 reviews of the impact of social media use on adolescent mental health in
order to identify global trends as well as gaps in the field. 15 of the reviews analyzed found a negative
effect on adolescent mental health and 15 reviews found mixed results. Only two reviews focused on
positive effects, such as a sense of belonging and inclusion. Negative impact on sleep quality emerged
as a primary effect of social media use, with 8 out of 32 reviews identifying this as a consequence.
Several limitations of current research in the field were identified, including confounding factors and
over-reliance on self reporting for measuring impacts. Future research should prioritize longitudinal
studies in order to strengthen claims about positive and negative effects of social media on mental health.
Additionally, further research should determine methods other than self reporting in order to collect more
accurate data.
| 67 |
Author(s):
Shreya Talukder.
Page No : 646-653
|
AutoLesion: Accessible AI-Based Classification of Skin-Lesions Using Custom Vision Language Models
Abstract
Skin cancer is the most common form of cancer worldwide, and early detection plays a critical role in
improving survival outcomes. While early-stage melanoma has a five-year survival rate of approximately
99%, this rate declines significantly at later stages, emphasizing the need for timely and accessible
diagnostic tools. However, access to dermatological care remains limited for billions of people worldwide
due to cost, geography, and time constraints. In this work, we present AutoLesion, an affordable and
accessible artificial intelligence–based system for the preliminary assessment of skin lesion malignancy
using a novel multimodal approach. Unlike prior methods that rely solely on dermoscopic or highresolution
imagery, AutoLesion integrates cell phone images with clinical and symptomatic metadata
through a fine-tuned vision–language model (VLM). This joint utilization of visual and clinical
information captures indications of malignant skin lesions that are often overlooked by image-only
models. We further introduce a test-time compute strategy to improve prediction accuracy and reliability.
Experimental evaluation on the ISIC (International Skin Imaging Collaboration) skin lesion archive
demonstrates that the proposed approach outperforms dermoscopic image-only baselines, supporting
the effectiveness of multimodal diagnosis from consumer-grade imagery. Even though these systems
can improve early detection, especially in disadvantaged regions, these advantages are outweighed by
practical and ethical concerns such as algorithmic bias, data privacy, and the need for human oversight.
These considerations underscore the importance of responsible development and deployment of AIassisted
medical diagnostic tools.
