Photovoltaic (PV) arrays under partial shading conditions (PSCs) suffer power degradation due to current mismatch and non-uniform irradiance. This paper proposes a novel Half-Shift Mirror (HSM) reconfiguration method for improving the power extraction from TCT connected PV arrays. The suggested method physically moves PV modules without changing the electrical connections and adopts a half-shift and mirror operations to satisfy row, column, diagonal, antidiagonal and sub-array uniqueness. The performance of the HSM technique is evaluated using 4 & times; 4 and 8 & times; 8 PV arrays under nine shading patterns, while a generalized reconstruction procedure is verified for a 6 & times; 5 non-square PV array under six shading conditions. For the 8 & times; 8 array, HSM achieves 10.69 to 20.92 kW GMPP with 0.01-0.43 kW mismatch loss and provides up to 33.38% power enhancement over TCT. The 6 & times; 5 array achieves 4.61-30.44% power enhancement under the investigated shading conditions. In addition, experimental validation using a 4 & times; 4, 640 W PV array under outdoor controlled shading conditions demonstrates GMPP enhancements of 1.31-84.73% over TCT. The results demonstrate that HSM provides effective shade dispersion, reduced row-current mismatch, local-peak suppression and improved power extraction while retaining the original TCT connections, confirming the scalability and practical applicability of the proposed HSM technique.
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.
