CVR College of Engineering

Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high-dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12

This study examines the influence of silicon carbide (SiC) particle size on the mechanical performance of AA7075/SiC composites fabricated via microwave sintering. Composites reinforced with SiC particles of varying sizes (60.7, 10.91, 5.33, and 0.73 μm) were synthesized, and their tensile strength, compressive strength, hardness, impact energy, and relative density were systematically evaluated. Microstructural characterization, including x-ray diffraction (XRD), was performed to elucidate the mechanisms governing the observed behavior. The results reveal a progressive improvement in mechanical properties with decreasing particle size. The composite reinforced with 0.73 μm SiC exhibited the highest tensile strength ( 362 MPa) and compressive strength ( 451 MPa), while intermediate particle sizes of 5.33 and 10.91 μm showed moderate improvements compared to the coarse 60.7 μm reinforcement, which exhibited comparatively lower strength. These improvements are primarily attributed to strong interfacial bonding, reduced porosity, and the activation of multiple strengthening mechanisms, such as the Orowan strengthening, and Zener grain pinning. Microwave sintering facilitated uniform densification and microstructural homogeneity, yielding a maximum relative density of 98.26

The high-pressure torsion (HPT) process is one of the most powerful methods of severe plastic deformation, capable of significantly refining the microstructure and altering the functional properties of high-strength aluminum alloys. In this work, the effects of HPT on the microstructure, residual stresses, hardness, and damping characteristics of the AA7075 alloy were comprehensively studied. Microstructural investigation revealed that the average grain size of the starting material was around 95 µm, but after HPT, it decreased to 6.1 µm, indicating continuous dynamic recrystallization. The secondary-phase particles were fragmented and uniformly distributed, as observed by SEM. The lattice strain was evident from the broadening of the XRD peaks. High compressive residual stresses were found near the surface ( − 600 MPa), whereas at greater depths the stresses were tensile due to strain gradients. Microhardness rose about 35–40

The objective of this research work is to evaluate the impact of cryorolling on the microstructure and mechanical properties of friction stir processed AA8011–B4C aluminum matrix composite. The study observed that cryorolling significantly reduced grain size, from 8.5 µm in the FSP condition to 5.3 µm after cryorolling ( 38

The most common kind of Dementia that affects social and cognitive abilities is Alzheimer’s disease (AD). In order to prevent brain damage and prolong daily functioning, early detection is essential for medical intervention. The current research has developed a novel Termite Cat Boost Prediction Framework (TCBPF) technique to detect Alzheimer’s disease using electroencephalogram (EEG) signals. The chief processes, like filtering, feature selection, prediction, and severity analysis, have been performed. The primary phase executes the filtering process to obtain a noise-free EEG signal. Here, the required features were extracted for the prediction process, and then the severity level of Alzheimer’s disease was finally determined. Subsequently, the performance of the proposed methods is compared with that of existing methods. The suggested model achieves the following metrics: 96.23