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Rahul Das

Publications and source records attributed to Rahul Das.

4 recordsLinked to original sources

Strong field lensing and shadows of $\boldsymbol{f(Q,\mathcal{B})}$ gravity black holes

We investigate the strong field lensing of light by black holes (BHs)emerging in $f(Q, \mathcal{B})$ theory of gravity, which is an extension of $f(Q)$ gravity theory by including a boundary term $\mathcal{B}$. Our analysis starts with the study of horizon structure for the BH spacetime in $f(Q,\mathcal{B})$ gravity. The strong field lensing analysis is done by employing the method developed by V.~Booza. We calculate the deflection angle for the static and spherically symmetric $f(Q,\mathcal{B})$ gravity BH spacetime and compare the results with the Schwarzschild BH. It is found that for all considerable scenarios of model parameters the $f(Q,\mathcal{B})$ gravity BH produces less deflection of light than the Schwarzschild BH. We extend our study to examine the outermost relativistic Einstein rings and three other observables, viz., angular position $\vartheta_{\infty}$, angular separation $s$ and relative magnification $r_\text{mag}$. We see that except $r_\text{mag}$, other observables show the same kind of behavioural change with respect to model parameters. We also study the shadow of the considered BH. Moreover, using the shadow data of BH Sgr A* from EHT collaborations, we constrain the model parameters of the $f(Q,\mathcal{B})$ theory for the BH spacetime. The constraining analysis reveals that for all considerable values of the model parameter $s_0$, the allowed range of the other model parameter $q_0$ is $-3\lesssim q_0 \lesssim -2$.

gr-qc

Resetting optimized competitive first-passage outcomes in non-Markovian systems

We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk (CTRW) framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired events. We characterize the efficiency of resetting through conditional mean first-passage times (MFPTs) and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times (FPTs), revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting.

cond-mat.stat-mech

Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework

We present AI4UI, a framework of autonomous front-end development agents purpose-built to meet the rigorous requirements of enterprise-grade application delivery. Unlike general-purpose code assistants designed for rapid prototyping, AI4UI focuses on production readiness delivering secure, scalable, compliant, and maintainable UI code integrated seamlessly into enterprise workflows. AI4UI operates with targeted human-in-the-loop involvement: at the design stage, developers embed a Gen-AI-friendly grammar into Figma prototypes to encode requirements for precise interpretation; and at the post processing stage, domain experts refine outputs for nuanced design adjustments, domain-specific optimizations, and compliance needs. Between these stages, AI4UI runs fully autonomously, converting designs into engineering-ready UI code. Technical contributions include a Figma grammar for autonomous interpretation, domain-aware knowledge graphs, a secure abstract/package code integration strategy, expertise driven architecture templates, and a change-oriented workflow coordinated by specialized agent roles. In large-scale benchmarks against industry baselines and leading competitor systems, AI4UI achieved 97.24% platform compatibility, 87.10% compilation success, 86.98% security compliance, 78.00% feature implementation success, 73.50% code-review quality, and 73.36% UI/UX consistency. In blind preference studies with 200 expert evaluators, AI4UI emerged as one of the leaders demonstrating strong competitive standing among leading solutions. Operating asynchronously, AI4UI generates thousands of validated UI screens in weeks rather than months, compressing delivery timeline

cs.SE

Effects of Internal Resonance and Damping on Koopman Modes

This study investigates the nonlinear normal modes (NNMs) of a system comprising of two coupled Duffing oscillators, with one oscillator being grounded and with the coupling being both linear and nonlinear. The study utilizes the eigenfunctions of the Koopman operator and validates their connection with the Shaw-Piere invariant manifold framework for NNMs. Furthermore, the study delves into the impact of internal resonance and dissipation on the accuracy of this framework by defining a continuous quantitative measure for internal resonance. The applicability and robustness of the framework for the systems which are very similar qualitatively to that of an ENO, are also observed and discussed about the limitations of the approximation technique.

nlin.CD