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  • AI & Machine Learning, Biology, Computing, Data & AI, Life Sciences
    Syn4MD: Aiding Medical Diagnosis through Image Synthesis and Classification
    Abstract Medical trainees depend on diverse visual reference material to develop diagnostic pattern-recognition skills, yet such material is often limited in diversity and accessibility. This paper presents a classifier-in-the-loop generative framework for synthesising realistic histopathology images: a Stable Diffusion v1.5 model is fine-tuned using Low-Rank Adaptation (LoRA) on the PathMNIST dataset, consisting of nine colorectal […]
    Kanishk Choudhary
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    August 3, 2026
  • Biology, Chemistry, Life Sciences, Physical Sciences
    DiaBreath: Multivariate Analysis of a Non-Selective Low-Cost Metal Oxide (MOX) Sensor Array for Breath-Based Diabetes Screening
    Abstract The number of diabetes patients is increasing worldwide, but many cases stay undiagnosed in low-resource regions. The A1C blood test is invasive and costly, therefore it is often not available in these regions. In this research, DiaBreath is proposed as a low-cost noninvasive screening system based on exhaled breath analysis. Volatile organic compounds (VOCs) […]
    Woo Sung Choi
    |
    July 24, 2026
  • Computing, Data & AI, Data Science, Economics, Social Sciences, Humanities & Policy
    A Statistical Physics Approach to Market Dynamics: Regime Shifts in Digital vs. Local Vendor Choice
    Abstract Digital commerce platforms compete with local vendors under frictions of time, price, and visibility. We test whether an interpretable choice model with reinforcement and social influence can yield substantial changes in aggregate market share as control parameters vary. We simulate repeated customer choice among vendors using a utility function that combines vendor attributes (price, […]
    Amaira Goyal
    |
    July 12, 2026
  • AI & Machine Learning, Computing, Data & AI
    Predicting Limited Service Zones Using GIS-Integrated Machine Learning: A Case Study of Three Cities
    Abstract Local businesses are important to neighborhood service access, but access to these services is limited in some areas. This study uses GIS and machine learning to identify possible limited service zones for restaurants and supermarkets across Dublin, Pleasanton, and Livermore in Alameda County, California. The model dataset included 1,134 Alameda County census block groups, […]
    Mandy Yuan
    |
    July 8, 2026

Most Discussed ›

NHSJS Reports ›

  • Health Sciences, Public Health
    Assessing the Level of Comfort of Southern California Middle and High School Nurses in Recognizing Early Warning Signs of Type 1 Diabetes
    Abstract Background: Type 1 diabetes is a life-threatening, autoimmune disease commonly found in pediatric patients. Although schools are the prominent environments in which symptoms are exhibited, studies are predominantly about dealing with an established condition, not the discovery of this disease. This study fills an important research gap by examining the familiarity, knowledge, and training […]
    Quinn Conroy
    |
    August 21, 2026
  • AI & Machine Learning, Computing, Data & AI, Economics, Finance, Social Sciences, Humanities & Policy
    Conformal Prediction Improves Model Reliability in the Prediction of Art Auction Price
    Abstract Predicting the price of art auction is an important question in finance and socio-cultural analytics because of surging interest in art valuation, decision-making for investment, and risk assessment for art market. Recently, deep learning technology has enabled prediction of valuation tasks for art auction prices based on image-related features. However, existing methodologies are limited […]
    Tekkyu Lee, David Lee
    |
    August 21, 2026
  • Biomedical Engineering, Engineering
    The Emerging Role of Lung Organoids in Advancing Personalised Medicine for Cystic Fibrosis
    Abstract Cystic fibrosis (CF) in the lung is a fatal disease stemming from mutations of the cystic fibrosis transmembrane conductance regulator gene (CFTR) that is detrimental to lung function. While traditional treatments were mostly symptomatic, CFTR modulators (CFTRm) now help some patients at the molecular level, targeting protein defects. An innovative approach in personalised medicine […]
    Rochele Lui
    |
    August 21, 2026
  • Life Sciences, Neuroscience
    Dynamic Functional Connectivity as a Candidate Neuroimaging Biomarker Feature for Mild Cognitive Impairment and Alzheimer’s Disease Progression
    Abstract Network neuroscience suggests that aspects of neurodegenerative disease progression can be explained by changes in how brain regions communicate, rather than solely by local neuronal damage. Traditional molecular models focused on amyloid and tau pathology have limited ability to explain variability in symptom timing, rate of cognitive decline, and cognitive reserve. The goal of […]
    Daniel Kao
    |
    August 21, 2026
  • Physical Sciences, Physics
    Finite-Size Spectral Signatures of Cavity-Induced Phase Behaviour in a Two-Site Tavis–Cummings Model
    Abstract This study investigates the finite-size spectral behaviour of the Tavis–Cummings model, a rotating-wave-approximation variant of the Dicke model describing N two-level atoms coupled to a single-mode cavity field. While cavity-induced phase behaviour has been studied extensively in the thermodynamic limit, it remains unclear whether minimal, exactly diagonalisable systems can reveal finite-size signatures of this […]
    Aryan Mathur
    |
    August 21, 2026

Life Sciences ›

Health Sciences ›

  • AI & Machine Learning, Computing, Data & AI, Data Science, Health Sciences, Sports Science
    A PCA-Based Framework for Injury Risk Profiling Using Basketball Training and Biomechanical Datasets
    Abstract Athlete monitoring for injury surveillance in basketball generates a large amount of data pertaining to player health and training. A number of collected variables may be correlated, which makes injury risk analysis challenging. In this study, a Principal Component Analysis (PCA) based framework was developed for injury risk profiling and evaluated using three publicly […]
    Sidhant Paruchuri
    |
    August 10, 2026

Physical Sciences ›

Computing, Data & AI ›

  • AI & Machine Learning, Computing, Data & AI, Earth & Environment, Sustainability
    Environmental Suitability Modeling for Solar Installations Using Convolutional Neural Networks
    Abstract As technology has increased, energy demand has also increased. Conventional methods of producing energy also produce harmful byproducts that damage the environment. Due to this issue, people have turned to renewable energy sources like solar power as an alternative. However, few consider the environmental impact of these solar panels. Most studies evaluate the production […]
    Aria Mitra
    |
    August 12, 2026

Engineering ›

  • Aerospace Engineering, Engineering
    Comparative Performance Analysis of Hollow and Annular Combustors in Rotating Detonation Engines
    Abstract Rotating detonation engines (RDEs) have emerged as a promising propulsion technology, offering superior thermodynamic efficiency over conventional Brayton-cycle engines due to their utilization of detonation combustion. Annular combustors (with inner and outer walls) are typically used in RDEs. However, hollow—lacking an inner wall—combustors are emerging as a promising alternative due to their potential mitigation […]
    Sidh Pandya
    |
    February 4, 2026

Earth & Environment ›

  • AI & Machine Learning, Computing, Data & AI, Earth & Environment, Sustainability
    Environmental Suitability Modeling for Solar Installations Using Convolutional Neural Networks
    Abstract As technology has increased, energy demand has also increased. Conventional methods of producing energy also produce harmful byproducts that damage the environment. Due to this issue, people have turned to renewable energy sources like solar power as an alternative. However, few consider the environmental impact of these solar panels. Most studies evaluate the production […]
    Aria Mitra
    |
    August 12, 2026

Mathematics & Statistics ›

  • Mathematics, Mathematics & Statistics
    Numerical Exploration of HOBO vs. QUBO: Using Sudoku as a Benchmark
    Abstract Sudoku, with its complex combinatorial structure, provides a natural NP-hard benchmark for testing optimization models. In this study, we quantitatively compare Quadratic Unconstrained Binary Optimization (QUBO) and Higher-Order Binary Optimization (HOBO). While QUBO is compatible with quantum computers, it is often evaluated on classical simulators known as quantum simulators which calculates with tensor network […]
    Naoaki Mochida
    |
    February 24, 2026

Social Sciences, Humanities & Policy ›

  • Finance, Social Sciences, Humanities & Policy
    Path Signatures for Regime Detection in Cryptocurrency Markets: A Rough-Path Framework Using Spot, Perpetual Basis, and Funding Rates
    Abstract Background. Cryptocurrency markets exhibit fast, reflexive regime transitions driven by leverage cycles, coordinated liquidations, and perpetual-swap funding imbalances. Classical regime-detection methods — hidden Markov models (HMMs) on returns and rolling-volatility thresholds — impose Markovian dynamics and summarize path information through low-dimensional moments, missing the path-dependent structure of crypto regime shifts.Methods. We develop a regime-detection […]
    Vibhun Naredla
    |
    August 20, 2026

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