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Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

Graphene oxide and its nanocomposites play a crucial role in various applications. Laser irradiation is a low-cost technique to tune the graphene oxide material, and a detailed study of the vibrational modes and structural changes during the laser-graphene oxide interaction is required. The evolution of defect modes and reduction process in graphene oxide at varying laser power and different exposure time duration, respectively, via Raman spectroscopy, is of interest in the present study. Graphene oxide (GO) is synthesized via Improved Hummer's method and characterized by X-Ray Diffraction (XRD), Thermogravimetric analysis (TGA), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray analysis (EDX), UV-Vis-NIR and Raman spectroscopy. The first-order Raman spectrum of GO consists of broad D and G peaks around 1350 and 1584 cm-1, respectively, and the broad second-order band around 2700 cm-1. Using the Lorentzian function, the first-order band is deconvoluted into five modes and the secondorder into four modes. The peak positions and FWHM of these modes undergo indicative changes. The variations in the intensity ratios of the defect modes and the (D'inf - Gapp) with laser power at different exposure time durations indicates the predominance of edge defects and reduction of graphene oxide, respectively. These results broaden the understanding of the effect of laser power over different time durations on the graphene oxide features. Our study provides quantitative information on the laser-GO interaction. [ABSTRACT FROM AUTHOR]

The interaction between peristaltic transport of a dusty fluid (Saffman's model) in a 2-dimensional uniform channel and the elasticity of the channel's flexible walls was studied under long wavelength approximation. Perturbation solutions were obtained for the stream functions of both the fluid particles and solid particles, in terms of the wall slope parameter. The expressions for average velocity of the fluid particles and solid particles, and average fluid flow rate were derived. The effects of various elastic parameters and mass concentration of dust particles on the streamline pattern and average flow rate were studied. The phenomenon of trapping was observed and the area of the trapped bolus increased along with the tension parameter, but decreased with damping and mass concentration of dust particles. [ABSTRACT FROM AUTHOR]

Our focus here is on examining the behavior of a boundary layer that regulates the movement of a non-Newtonian fluid undergoing a chemical reaction on a radiative paraboloid surface. This includes studying the fluid’s movement, temperature, and mass transfer. The fluid being studied pertains to the Williamson fluid model, which is described as a type of extended Newtonian fluid. We have considered the effects of Joule heating and dissipation through viscous phenomena based on the rules of heat and mass movement. The equations in the mathematical structure of partial differential equations (PDEs) governing the fluid continue to flow model in the problem. A subsequent evolution of these equations into ODEs by incorporating similarity variables. The velocity field’s schematic behavior is attributable to the magnetic parameter, thickness parameter, and the Weissenberg number, which exhibit velocity profile decline. In this research, both thermal radiation and Eckert number are highlighted, and it is concluded that both of these variables substantially raise the temperature field. We continue to track the concentration-reducing chemical reaction characteristic that remains tracked along with concentration transfer. Furthermore, the study includes a comprehensive presentation of comparative analyses with previously published scholarly works. [ABSTRACT FROM AUTHOR]