arXiv subjects

Nayana Mukherjee

Publications and source records attributed to Nayana Mukherjee.

3 recordsLinked to original sources

arXiv2026

Reaction-transport coupling drives spatiotemporal organization in fuel-driven supramolecular polymerization

Chemically fueled supramolecular systems provide a versatile platform for generating nonequilibrium structures and dynamical instabilities, including chemical oscillations and traveling waves reminiscent of biological organization. However, a minimal mechanistic framework capable of capturing the emergence of such spatiotemporal order is still lacking. Here, we develop a minimal reaction-transport framework for fuel-driven supramolecular polymerization that couples activation-deactivation chemistry with cooperative assembly, fragmentation, and polymer length-dependent diffusion. The model captures autonomous oscillations arising through a Hopf bifurcation and demonstrates how temporal instabilities evolve into spatial self-organization upon inclusion of transport. We show that the nonlinear interplay between reaction kinetics and state-dependent mobility gives rise to traveling polymerization fronts, oscillatory wave dynamics, and complex spatiotemporal patterns. The propagating fronts exhibit near-ballistic dynamics, revealing a fundamentally nonequilibrium transport mechanism emerging from reactive feedback and dynamically evolving diffusivity. These findings establish a minimal physical framework connecting dissipative self-assembly, nonlinear transport, and active matter, while providing design principles for programmable supramolecular materials capable of autonomous spatiotemporal organization.

cond-mat.soft↗

arXiv2026

Cluster-Based Bayesian SIRD Modeling of Chickenpox Epidemiology in India

This study presents a cluster-based Bayesian SIRD model to analyze the epidemiology of chickenpox (varicella) in India, utilizing data from 1990 to 2021. We employed an age-structured approach, dividing the population into juvenile, adult, and elderly groups, to capture the disease's transmission dynamics across diverse demographic groups. The model incorporates a Holling-type incidence function, which accounts for the saturation effect of transmission at high prevalence levels, and applies Bayesian inference to estimate key epidemiological parameters, including transmission rates, recovery rates, and mortality rates. The study further explores cluster analysis to identify regional clusters within India based on the similarities in chickenpox transmission dynamics, using criteria like incidence, prevalence, and mortality rates. We perform K-means clustering to uncover three distinct epidemiological regimes, which vary in terms of outbreak potential and age-specific dynamics. The findings highlight juveniles as the primary drivers of transmission, while the elderly face a disproportionately high mortality burden. Our results underscore the importance of age-targeted interventions and suggest that regional heterogeneity should be considered in public health strategies for disease control. The model offers a transparent, reproducible framework for understanding long-term transmission dynamics and supports evidence-based planning for chickenpox control in India. The practical utility of the model is further validated through a simulation study.

stat.AP↗

arXiv2025

Bayesian Hybrid Machine Learning of Gallstone Risk

Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effective clinical management. Although logistic regression remains a standard tool for assessing associations between predictors and gallstone status, it often underperforms in high-dimensional settings and may fail to capture intricate relationships among variables. To address these limitations, we propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection. Specifically, Adaptive LASSO is employed to identify a sparse and interpretable subset of influential features, followed by Bayesian Additive Regression Trees (BART) to model nonlinear effects and uncover key interactions. Selected interactions are further characterized by physiological knowledge through differential equation-informed interaction terms, grounding the model in biologically plausible mechanisms. The insights gained from these steps are then integrated into a final logistic regression model within a Bayesian framework, providing a balance between predictive accuracy and clinical interpretability. This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.

stat.AP↗