Convective boundary conditions act as a link between the fluid and its surroundings, altering wall temperatures and temperature gradients, which in turn directly influence the critical Rayleigh number and system stability. In this context, the novelty of the present study lies in investigating the convective instability of a bi-viscous Bingham fluid flowing through a horizontal porous channel with vertical throughflow, subject to convective thermal boundary conditions at both walls. After introducing appropriate non-dimensional scales, infinitesimal disturbances are superimposed on the basic steady-state flow, and linear stability analysis is performed to examine the resulting stability characteristics of the system. The corresponding eigenvalue problem associated with the neutral stability condition is numerically solved using the bvp4c solver in MATLAB. The results show that system stability increases monotonically as stronger external heating is applied at the bottom wall, irrespective of whether the external heating at the top wall increases or decreases. The system remains stable only when the heating at the bottom wall is dominated by stronger external heating at the top wall; otherwise, instability occurs. Furthermore, the study examines the onset of convection under two limiting boundary conditions: one boundary prescribed with a heat flux and the other maintained at an isothermal state. Finally, it is found that the onset of convection occurs earlier as the bi-viscous Bingham parameter increases for all considered boundary conditions.
Brain–computer interfaces (BCIs) that use motor imagery (MI) present a viable avenue for applications involving direct neural control. However, because of their complicated spatiotemporal patterns and low signal-to-noise ratio, EEG signals continue to present a barrier for high classification accuracy. In this work, a novel convolution neural network–transformer hybrid architecture is proposed to enhance MI EEG classification. The CNN layers collect local temporal and spatial characteristics, whereas the transformer encoder captures global temporal relationships. A channel attention mechanism and gray wolf optimization (GWO) are integrated to further refine feature extraction and hyperparameter tuning. The proposed framework performs better than a number of cutting-edge baselines, as evidenced by experimental validation on the datasets for BCI Competition IV 2a and 2b, which shows considerable gains in accuracy, F1-score, and kappa coefficient. The findings validate the model’s capacity for reliable and broadly applicable MI decoding.
The microwave absorption properties of barium hexaferrite composite with multiwall carbon nanotubes (MWCNTs) and reduced graphene oxide (rGO) have been investigated. The hexaferrite sample was prepared via a one-step co-precipitation method using NaOH as the precipitating agent. The prepared ferrite showed irregular spherical morphology. The conductive fillers, such as MWCNTs and rGO, have been used to enhance the absorption properties of barium hexaferrite. It has been seen that 5
The escalating electromagnetic (EM) wave pollution has necessitated the development of advanced EM wave absorbing materials, making the understanding of their loss mechanisms crucial. The interactions between external radiations and absorbing materials require a detailed study of dielectric and magnetic losses and their underlying mechanisms. Investigating these attenuation mechanisms is essential for the development of novel absorbers. While most research has concentrated on the synthesis of absorbing materials, there is a notable lack of studies that explain the specific loss mechanisms involved. This review offers a thorough examination of dielectric losses, including interfacial, dipolar, and defect-induced polarizations, as well as magnetic losses such as hysteresis and multiple resonances (domain wall, exchange, and natural resonances). Additionally, it incorporates an assessment of morphological and unified synergistic loss mechanisms, providing a comprehensive understanding of the topic. The study covers advanced design strategies, including meta-structures and 3D-printed morphologies, to optimize material interactions with incident EM waves. By integrating insights from various loss mechanisms, this review provides a comprehensive framework for designing next-generation EM wave absorbers with enhanced efficiency, bandwidth, and stability. The research addresses critical challenges in EM wave absorption, offering valuable guidance for researchers and engineers in developing advanced materials for multifunctional applications.
This investigation analyzes the effects of variable viscosity and rotation on the onset of thermal convection in a non-Newtonian Navier–Stokes–Voigt (NSV) fluid, which has not been addressed in the available literature. The threshold for convective instability is obtained by linearizing the governing equations related to the perturbations. The consequential eigenvalue problem is then solved both analytically and numerically through the Galerkin process. The necessity of the Kelvin-Voigt factor, the viscosity variation factor, the Taylor number and the Prandtl number on the critical stability parameters is exhaustively examined. It is detected that above the certain threshold assessment of the Taylor number, the instability creates in as oscillatory type. The threshold range of the Taylor number at which oscillatory motion probable enhances with increasing the viscosity variation factor while, it declines with increasing the Prandtl number. The stability of the arrangement increases with increasing the Taylor number and the Prandtl number whereas, it decreases with the viscosity variation factor and the Kelvin-Voigt factor. The dimension of the convective cells declines with increasing the Taylor number, the viscosity variation factor and the Prandtl number while, it upsurges with the Kelvin-Voigt factor if the value of the Prandtl number is more than one.
