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 presents a detailed investigation of Ni/Cu/Fe2O3:Bi2O3/n-GaN metal–oxide–semiconductor (MOS) heterojunctions, focusing on their structural, chemical, and electrical properties. Fe2O3:Bi2O3 composite films were successfully deposited on n-GaN substrates, as confirmed by glancing-angle X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), which verified the film’s crystallinity, composition, and the formation of a uniform insulating layer. XPS further confirmed the presence of key elements and proper interface formation between the metal electrodes and the semiconductor. Electrical measurements revealed that the MOS exhibited clear rectifying behavior with reduced leakage current compared to the conventional Schottky junction (SJ). Analysis of key parameters including Schottky barrier height (Φb), ideality factor (n), and series resistance (Rₛ) was conducted using multiple extraction methods (Cheung’s, F(V)–V, and ΨS–V), all showing good agreement. The forward I–V characteristics of both SJ and MOS HJs demonstrated ohmic behavior at lower voltage regions, transitioning to space-charge-limited conduction (SCLC) at higher voltages. This transition confirms the influence of interface states and trap-assisted conduction in determining the electrical transport mechanism. These results demonstrate the effectiveness of Fe2O3:Bi2O3 nanocomposites as insulating layers in GaN-based MOS devices and underscore their potential for future optoelectronic applications.
The current study explores an analysis of unsteady thermo-viscous fluid flow around a moving horizontal cylindrical surface. The modeling of partial differential equations pertaining to the flow through cylindrical objects is coupled, complex, and non-linear in nature. These equations have been analyzed for the solutions of velocity and temperature fields. The numerical techniques used so far in literature are very complex; moreover, they are not giving accurate, convergent solutions because of their own disadvantages. These methods also require more time, high computational cost, and processing resources to obtain the required results. The available analytical methods to solve these kinds of equations are not applicable since these are simultaneous, highly non-linear equations. The tool NDsolve, developed in Mathematica software, is utilized to solve these problems in the current study. The code of the algorithm has been developed in this software via. Runge–Kutta finite difference 6th-order method to obtain the results of the modeling equations. The results obtained have been depicted in graphical illustrations and tabular data. The numerous values of the material constraints such as viscosity coefficient, temperature gradient factor, thermo-mechanical interaction factor, strain thermal conductivity factor, pressure gradient factor, and Fourier thermal conductivity constraint effects have been studied as graphical plots and tabular data. Additionally, numerical findings of the governing equations for a fluid’s slow, steady motion have been obtained, and comparison is made with the current available literature results; they show great agreement. The modeled governing equations of the present study have been observed in the studies of flows through the pipeline systems and nuclear reactors.
The present study investigated the methylene blue dye removal from aqueous solutions using biochar produced from raw coconut shell and subsequent chemically modified biochar. Sulphuric acid and sodium hydroxide are the two chemicals used for the activation of the raw biochar. The batch experiments were conducted by varying the operating conditions, namely pH, biochar dose, initial dye concentration, temperature, contact time, and rotating speed. To understand the surface morphology, functional groups, crystalline nature and elemental composition of the biochar, scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDAX), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and carbon, hydrogen and nitrogen (CHN) analysis were used. The adsorption mechanism and rate of adsorption were investigated through adsorption isotherms and kinetic studies. The maximum removal efficiency of 98
Natural fiber composites tend to experience the problem of unpredictable mechanical strength, poor thermal stability and improper predictive modelling, which does not allow them to be used in high-tech applications. The overall aim of the proposed research is to produce and optimize the hybrid jute/ramie epoxy composites by using state-of-the-art machine learning algorithms, namely the Multi-Directed Differentiated Attention Parallel Dual-Channel Bi-Directional Long Short-Term Memory (ADD-BiLSTM) model to forecast and enhance the mechanical, thermal and moisture resistance characteristics of the composite. The research examines hybrid jute/ramie epoxy composites made in 5 weight ratios through hand lay-up. Sample A (3:1) demonstrated the best tensile (34.5 MPa) and flexural strength (54 MPa), which is followed by Sample E (0:4) with the best impact energy (21 J) and hardness (72 BHN). A hybrid neural network model is designed and trained by the Multi-Scenario Chaotic Crested Ibis Algorithm (MSCCIA) in order to increase its predictive power. Python-based simulations demonstrated that the proposed approach achieved a lower Root Mean Square Error (RMSE) (0.08) and higher accuracy (94
