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Erbium-trioxide (Er2O3) and magnesium oxide (MgO) nanocomposite were effectively synthesized in the form of crystalline powder using a microwave irradiation approach. Various techniques were employed for identifying crystalline structure, FTIR fingerprint region, fluorescence emission behaviors, and surface morphology, dielectric, and magnetic properties using PXRD, FTIR spectroscopy technique, fluorescence spectroscopic technique, SEM analysis, frequency vs. dielectric constant, and MH curve analysis, respectively. Confirmation of the metal-oxygen bond such as Er2O3, and MgO was established through the analysis of stretching frequencies in the FTIR spectrum. The PXRD results using Rietveld refinement confirmed the crystalline nature of the synthesized nanoparticles, consisting of Er2O3, and MgO with unit cell compositions ~ 94.12 and ~ 5.88%, respectively. SEM imaging provided insights into the morphology of the particles, revealing a spherical shape with noticeable agglomeration. The elemental compositions such as Erbium (Er) and Oxygen (O), were validated by the EDS spectrum, confirming the successful achievement of Er2O3, and MgO nanoparticle in the synthesized composite. In addition, the vibrating sample magnetometer (VSM) graph illustrated the paramagnetic behavior of the doped Er2O3/MgO composite at room temperature. The thorough examination of the synthesized Er-MgO NPs, covering structural, morphological, and magnetic characteristics, contributes to a comprehensive understanding of their properties. [ABSTRACT FROM AUTHOR]

Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI'2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes. [ABSTRACT FROM AUTHOR]