Lately, a new class of nanofluids, namely hybrid nanofluids, has been introduced that performs much better compared with the nanofluids when a healthier heat transfer rate is the objective of the study. Heading in the same direction, the present investigation accentuates the unsteady hybrid nanofluid flow involving CuO, Al₂O₃/C₂H₆O₂ achieved by an oscillating disk immersed in the porous media. In a study of the homogeneous and heterogeneous reactions, the surface catalyzed reaction was also considered to minimize the reaction time. The shape factors of the nanoparticles were also taken into account, as these play a vital role in assessing the thermal conductivity and heat transfer rate of the system. The assumed model is presented mathematically in the form of partial differential equations. The system is transformed by invoking special similarity transformations. The Keller Box scheme was used to obtain numerical and graphical results. It is inferred that the blade-shaped nanoparticles have the best thermal conductivity that boosts the heat transfer efficiency. The oscillation and surface-catalyzed chemical reactions have opposite impacts on the concentration profile. This analysis also includes a comparison of the proposed model with a published result in a limiting case to check the authenticity of the presented model.

Here, we discuss three-dimensional dusty nanofluid thin film flow with nonlinear thermal radiation, where carbon nanotubes flow past an inclined rotating disk with a constant angular velocity of Ω. This novel mathematical model is unique and is discussed here for the first time. Downward draining flow and lateral flow arise due to inclination. The demonstrated geometry is characterized in terms of time-independent continuity, momentum, and energy balance. Similarity transformations convert the partial differential equation into a system of ordinary differential equations. The obtained equations are analyzed numerically using the bvp4c MATLAB function. The thermal field of the dust phase was smaller than that of the nanofluid phase, and this difference was exacerbated by increasing the thermal radiation. To validate the model presented here, it is compared to a previous model; the models showed high concordance.

In the present exploration, our objective is to investigate the importance of Hall current coatings in the establishment of Cattaneo–Christov (CC) heat flux model in an unsteady aqueous-based nanofluid flow comprising single (SWCNTs) and multi-walled (MWCNTs) carbon nanotubes (CNTs) amid two parallel rotating stretchable disks. The novelty of the presented model is strengthened with the presence of homogeneous-heterogeneous (HH) reactions and thermal stratification effects. The numerical solution of the system of coupled differential equations with high nonlinearity is obtained by applying the bvp4c function of MATLAB software. To corroborate the authenticity of the present envisioned mathematical model, a comparison table is added to this study in limiting case. An excellent harmony between the two results is obtained. Effects of numerous parameters on involved distributions are displayed graphically and are argued logically in the light of physical laws. Numerical values of coefficient of drag force and Nusselt number are also tabulated for different parameters. It is observed that tangential velocity (function of rotation parameter) is increasing for both CNTs. Further, the incremental values of thermal stratification parameter cause the decrease in fluid temperature parameter.

Tuberculosis (TB), a contagious disease, significantly affects lungs functioning. Amongst multiple detection methodologies, Chest X-ray analysis is considered the most effective methodology. Traditional Deep Learning methodologies have shown good results for TB detection; however, model’s huge number of parameters, size, and compute requirements making it unsuitable for its practical deployment. Owing to scarce annotated datasets in medical domain augmented datasets are generated which is not a recommended technique in medical domain. This study presents TB-FSNet consisting of Few Shot Learning - Prototypical Network (FSL-PT) with a modified MobileNet-V2 backbone, incorporating a Self-Attention layer. The significant contribution of this study is to effectively train TB-FSNet in FSL-PT paradigm with six different backbones. The dataset utilised for this study consists of Montgomery County, and Shenzhen Chest X-ray Dataset combined. The proposed method attains highest accuracy of 93.6% with mere 2.21M parameters and 8.67 MB size, while maintaining high performance metrics such as precision, specificity, and sensitivity. Moreover, TB-FSNet is designed for seamless integration into embedded devices, making it suitable for deployment on edge devises. The model processes Chest X-ray images in real-time, providing immediate confidence scores for disease detection. This capability ensures that users can receive accurate diagnostic insights without needing to wait for medical professionals, enhancing the accessibility and efficiency of TB detection.