Bajkul Milani Mahavidyalaya

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.

Two prescriptions for the construction of Carroll geometries, the expansion of geometric variables near horizon and the expansion of metric with zero limit of the expansion parameter c (speed of light in vacuum), are known to complement each other. The entropy of an ideal gas, confined in a box and kept very close to the horizon, depends on the transverse area of the container. We show this by using the Carroll geometry constructed through the expansion of the metric and then taking the zero limit of the expansion parameter c. Therefore, the present analysis assures the complementing nature of two ways of finding the Carroll geometry from the thermodynamical point of view.

The present investigation offers a novel approach by scientifically revalidating traditional beliefs surrounding the medicinal value of Bengal amloki (Phyllanthus emblica L.) through comprehensive physicochemical, spectroscopic and chromatographic analyses. This study aimed to determine the bioactive potential and establish traditional beliefs within the framework of scientific interpretation, and to characterize the physicochemical and spectroscopic properties of the fruits of Bengal amloki. The fruits' chemical analysis was done to assess their bioactive potential as a source of alternative medicine and their ability to treat ailments. The nutritious fruits of Bengal amloki (also known as amla or Indian gooseberry) revealed C, H, O and N contents of 48.773 +/- 0.211%, 4.858 +/- 0.222%, 42.411 +/- 0.447% and 1.844 +/- 0.057%, respectively. Based on Fourier Transform Infrared Spectroscopy (FTIR) results, strong bonds between C-O, O-H, N-H, O=C=O, C-H, and O-H molecules supported the presence of primary alcohol, carboxylic acid, alkene, carbon dioxide, alkane and phenol, respectively. Several significant bioactive phyto-constituents have also been detected by Gas chromatography-mass spectrometry (GC-MS) screening, whose bioactivities are believed to be useful in the management of various disorders. The results showed that biomass with high fixed carbon (FC), high volatile matter (VM) and low Compositional analysis (CA) had the best efficiency, antioxidant properties, and potential for converting energy.

Trihybrid ferro-nanofluids, combining ternary magnetic nanoparticles with motile microorganisms in a porous environment, represent a frontier class of engineered fluids with significant relevance to biomedical microdevices, solar thermal collectors, and bioreactor engineering. This study numerically investigates the coupled effects of a magnetic dipole interaction, Cattaneo–Christov (CC) double diffusion, Darcy–Forchheimer porous resistance, activation energy, and bioconvection on the boundary layer flow of a Casson trihybrid ferro-nanofluid over a linearly elastic stretching surface. The working fluid is a water–ethylene glycol (WEG) mixture loaded with ternary ferromagnetic nanoparticles ( Fe_3O_4 , CoFe_2O_4 , MnFe_2O_4 ) and gyrotactic microorganisms, and convective boundary conditions are imposed for heat, mass, and microorganism transport. Thermal and solutal transport is governed by the CC heat and mass flux formulations, which introduce finite relaxation times and thereby capture non-Fourier conduction and non-Fickian diffusion. The governing coupled nonlinear partial differential equations are reduced to a system of ordinary differential equations via appropriate similarity transformations and solved numerically with MATLAB’s bvp4c collocation solver. The results reveal that ferrohydrodynamic interactions suppress fluid motion, elevate temperature, increase skin friction, and reduce heat transfer rates. Enhanced Forchheimer inertia further strengthens flow resistance and wall shear stress. Thermal relaxation improves surface heat transfer despite delaying heat flux propagation, whereas solutal relaxation reduces concentration levels and mass transfer rates. Increasing activation energy weakens reaction kinetics, leading to lower Sherwood numbers. Furthermore, larger Péclet numbers promote advective transport, reducing near-wall microorganism concentration and enhancing microbial density transport. The present framework offers a systematic numerical characterization of the interplay between ferrohydrodynamic, bioconvective, and non-Fourier transport mechanisms, providing a theoretical basis for future application-specific investigations in advanced thermal management and biomedical systems.

The number of production companies in the world is growing day by day, and they produce a huge amount of waste that harms the environment. Production companies look for various solutions to manage waste. Additionally, this produced waste can be reused in the remanufacturing process. In this regard, the governments of developed countries provide subsidies to manufacturer on returned products to encourage them to remanufacture. This work proposes a dual-channel closed-loop supply chain model in which products are produced for circular economics. Dual channel refers to the combination of one direct online channel, where manufacturer sells new products to customers directly through his own E-marketplace with an E-ad platform and the traditional retail channel, where the retailer sells new products to customers offline after purchasing them from the manufacturer. Moreover, customers can resell the used products to the manufacturer's collection center for recycling products at an exchange price. The manufacturer will then remanufacture or rework the goods following inspection and sell them on the secondary market. This work provided a concise idea, supported by precise data (recorded by blockchain technology), about the circular economic index (CEI) of products to address the trust issues of consumers. The primary objective of this study is to maximize the profit of the supply chain while reusing substantial amounts of waste produced every day and conserving natural resources. Here, the return rate is considered linearly dependent on CEI, which helps the manufacturer make the environment sustainable and increases the profit of supply chain members by increasing demand and government subsidies. The proposed model is formulated mathematically, and both centralized and decentralized methods are used to solve the model. In the decentralized model, Stackelberg game theory approach is applied to solve the corresponding maximization problems. Also, the revenue-sharing contract policy is employed to achieve coordination between the retailer and manufacturer. Here, the numerical results indicate that the revenue-sharing contract model is more acceptable from each member's perspective. However, the total supply chain profit is slightly higher in the integrated model than in the contract model. Finally, through sensitivity analysis, we observe which key parameters are more effective for which variables or profitability.