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Divyansh Tripathi

Publications and source records attributed to Divyansh Tripathi.

4 recordsLinked to original sources

Coalescence of Polymer Droplets Moving on a Surface with Stiffness Gradient

Here, we study the coalescence of two droplets that are moving in the same direction on a soft surface; the motion of the droplets is caused by a gradient in the surface stiffness. As reference, stationary coalescence of the same droplets is also studied on the corresponding uniform surfaces for different stiffness values. To describe the coalescence phenomenon on a surface with stiffness gradient, a relevant range of velocity ratios of the leading and the trailing droplet was considered to elucidate the effect of this parameter on coalescence. Moreover, to analyze the dynamics of the process, the temporal growth of the bridge height $(h)$ was investigated, which follows a power law $(h \sim t^{\alpha})$, before eventually attaining a constant value. The obtained values of $\alpha$ show a transition from a higher to a lower value as a function of time, pointing to the presence of two distinct power-law growth regimes, where the transition signifies the crossover from the capillarity-dominated regime to the viscoelasticty-dominated regime of coalescence. In addition, varying attractive strengths for droplet--droplet and intra-droplet interactions were considered. The results indicate that both the dynamics and the degree of the coalescence strongly depend on these interaction parameters. Thus, we anticipate that our results will shed more light on the durotaxis-driven coalescence of polymeric droplets for various relevant system parameters, which will have practical implications for applications ranging from microfluidics to ink-jet printing, where substrate properties may vary. In addition, results may add to the fundamental understanding of the interactions among multicellular aggregates moving on biological surfaces.

cond-mat.soft

MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System

As Agentic AI gain mainstream adoption, the industry invests heavily in model capabilities, achieving rapid leaps in reasoning and quality. However, these systems remain largely confined to data silos, and each new integration requires custom logic that is difficult to scale. The Model Context Protocol (MCP) addresses this challenge by defining a universal, open standard for securely connecting AI-based applications (MCP clients) to data sources (MCP servers). However, the flexibility of the MCP introduces new risks, including malicious tool servers and compromised data integrity. We present MCP Guardian, a framework that strengthens MCP-based communication with authentication, rate-limiting, logging, tracing, and Web Application Firewall (WAF) scanning. Through real-world scenarios and empirical testing, we demonstrate how MCP Guardian effectively mitigates attacks and ensures robust oversight with minimal overheads. Our approach fosters secure, scalable data access for AI assistants, underscoring the importance of a defense-in-depth approach that enables safer and more transparent innovation in AI-driven environments.

cs.CR

A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification

This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS observational patterns and employing a dedicated convolutional neural network (CNN), we achieve state-of-the-art performance for star galaxy classification. Our preliminary results demonstrate a promising pathway for efficient and precise astronomical analysis, especially in real-time observational settings.

astro-ph.IM

Effect of Magnetised Discontinuity on Diffusive Shock Acceleration

We investigate the impact of magnetic fields and diffusion mechanisms on the energy spectra of particles accelerated via diffusive shock acceleration. We analyse magnetised shock jump conditions and demonstrate how magnetisation and angular dependence modify upstream and downstream velocities, which enter the transport equation within a Monte Carlo simulation framework. We consider constant, momentum-dependent, and pitch-angle-dependent diffusion coefficients to assess their influence on particle acceleration. Our results show that magnetic fields enhance particle confinement and facilitate more efficient energy gain. In the absence of magnetisation, particle spectra tend to be steeper due to rapid escape and weaker scattering effects, whereas magnetised shocks systematically produce flatter spectra across all diffusion models. Among them, pitch-angle-dependent diffusion leads to the strongest spectral flattening, underscoring its role in sustaining extended acceleration. It is also seen that an increased upstream pressure, associated with enhanced magnetic turbulence, broadens the spectral range by improving particle scattering efficiency and enabling multiple shock crossings. As the shock inclination angle increases, the velocity contrast between upstream and downstream regions diminishes, modulating the spatial extent of the acceleration zone. Notably, pitch-angle-dependent diffusion remains robust under varying shock conditions, ensuring sustained acceleration.

astro-ph.HE