University of Gour Banga

Building on the neoclassical growth model, the paper develops an analytical framework that links financial development, investment, openness, total factor productivity (TFP), and human capital to per capita income and economic growth. It then calibrates this framework using Indian macroeconomic time series from 1981 to 2021. Results from Johnson's multivariate framework reveal long-term linkages among various specifications of these variables. The study then explores short- and long-term growth trajectories, expanding the cointegrated framework. It finds that financial development has a dual effect: a growth-accelerating short-term impact and a long-term growth-retiring effect. Foreign Direct Investment (FDI) shows a long-run growth effect but a detrimental impact in the short run. Domestic investment influences growth only in the long term. The findings also support the idea that TFP plays a key role in the growth trajectory. Additionally, trade openness harms long-term growth, while the human capital index has a relatively weak short-term growth effect. The robustness of these results is verified through the Wald test of Granger causality. The paper discusses the macroeconomic implications of these empirical findings, some of which differ from the theoretical perspectives outlined in the study.

This study investigates the flow and thermal dynamics of Casson milk enhanced with silver-magnesium oxide hybrid nanoparticles within a rapidly activated electromagnetically actuated conduit under quadratic thermal ramping and oscillatory pressure forcing. A physics-based model incorporating thermal radiation, volumetric heat absorption, and Darcy porous drag is solved analytically using the Laplace transform technique, with predictions validated by a Python-based artificial neural network (ANN). The electromagnetic conduit flow is mathematically modeled, with solutions obtained via Laplace transform analysis. Results reveal that nanoparticle inclusion significantly improves effective thermal conductivity and alters viscosity, enhancing heat transfer efficiency while modifying velocity profiles. Key parametric trends show that the modified Hartmann number amplifies flow momentum, whereas wider electrode spacing attenuates it. Increased thermal radiation reduces fluid temperature, while a larger Casson parameter abates shear stress (SS). The radiation parameter positively augments the rate of heat transfer (RHT). The developed ANN model demonstrates exceptional predictive accuracy, achieving over 99.93% agreement with analytical results across training, validation, and test datasets for both SS and RHT predictions. These findings highlight the synergistic potential of hybrid nanofluids and AI-driven modeling for optimizing thermal processing, improving energy efficiency, and advancing sustainable practices in the dairy industry.

This study explores the nonlinear hemodynamics of ternary hybrid nanoparticles (THNPs) in a diverging, ciliated microtube, accounting for interfacial nanolayer effects, electromagnetic forces, and cilia-driven propulsion. A fractional second-grade fluid model captures memory-dependent viscoelastic behavior. The governing nonlinear equations are analytically solved via the Homotopy Perturbation Method (HPM), yielding rapidly converging series solutions. Results show that the fractional parameter enhances axial velocity, while relaxation time impedes flow near the tube center. Lorentz forces boost flow rates, whereas Hall and ion-slip currents exert a dampening effect. Meanwhile, nanolayer interactions intensify wall shear stress but hinder thermal efficiency by reducing heat transfer. A comprehensive dataset derived from the HPM solution was used to train, test, and validate an artificial neural network using the Backpropagation Levenberg-Marquardt scheme (ANN-BPLMS), achieving high predictive accuracy (99.99% testing, 99.996% cross-validation). These insights have significant implications for magnetically guided drug delivery, artificial cilia systems, and nanoscale biomedical transport. By integrating fractional dynamics, electrokinetics, and interfacial physics, this work advances the modeling of complex physiological microflows.

This study presents a deep learning framework to simulate gold-maghemite nano-blood flow in a rotating electromagnetically actuated microchannel, targeting applications in advanced atherectomy devices. The model integrates key multiphysics phenomena, including Hall and ion-slip currents, thermal emission, and non-Newtonian hemodynamics. The governing flow equations, solved using the Laplace Transform (LT) method, predict velocity, temperature, and shear stress profiles under physiological conditions. Key findings demonstrate that increasing the rotation and ion-slip parameters suppresses streamwise blood flow while enhancing cross-stream flow. Higher radiation and frequency parameters significantly elevate blood temperature. Rotation creates a dual shear stress effect: enhancement via secondary flow shear stress (SSSF) and reduction via primary flow shear stress (SSPF). Thermally, the rate of heat transfer (RHT) is inhibited by high radiation but facilitated by a process of progressive heat absorption. An artificial neural network (ANN) model achieves high predictive accuracy (up to 100% in RHT testing), bridging computational fluid dynamics with clinical device innovation through explainable artificial intelligence (AI) and offering a paradigm shift in cardiovascular intervention design.

This research paper explores the innovative application of artificial intelligence (AI) in understanding the behaviors of silver and magnesium oxide nanoparticles within milk flow. This study utilizes a specially designed vibrating electromagnetic channel to observe the effects under controlled parabolic thermal ramping and oscillatory pressure variations. This framework couples essential physical mechanisms-radiative emission, thermal sinks, and porous matrix interactions-where Darcy's law quantifies the permeability-driven viscous drag. The mechanics of milk flow through an electromagnetically activated channel are meticulously formulated and solved using mathematical and computational methods, with the Laplace transform (LT) technique facilitating a streamlined solution to the equations. The analysis concentrates on flow metrics, presenting results through detailed graphical representations. Significant findings comprise the enhancement of thermal conductivity and flow viscosity due to the nanoparticles, which improve heat transport efficiency and modify flow patterns. The operational control of milk flow dynamics shows dual dependencies-momentum amplification via electromagnetic intensity (Hartmann number) versus suppression through electrode spacing, while thermal management reveals frequency-dependent shear stress (SS) augmentation and rate of heat transfer (RHT) enhancement through optimized heat uptake parameter. An artificial neural network (ANN) is calibrated to emulate the LT solver's outputs for wall SS and RHT. The ANN achieves high fidelity (R2>0.99) in predicting these metrics across the parameter space explored in the LT simulations, but its generalization to experimental or real dairy systems remains unvalidated and is a focus of future work. The key findings demonstrate the potential of integrating advanced materials and AI technologies to improve product characteristics and processing efficiency.