The automotive, biomedical, and aerospace industries are among those with a rising need for lightweight materials with enhanced mechanical and tribological qualities. Composites based on magnesium alloys have attracted interest because of their excellent strength-to-weight ratio and promise to improve component performance. Magnesium (Mg) alloy-based composites find applications in sports and leisure equipment, aerospace, biomedical implants, and more. The research outlined here serves a critical need in the field of materials science and engineering, particularly regarding the development of advanced magnesium (Mg) alloy-based composites. In this study, we have created a new aluminum composite using the AZ31 alloy mixed with 5% boron carbide (B4C) and 5% molybdenum disulfide (MoS2) as reinforcement through a powder metallurgical technique. The magnesium alloy contains 3% aluminum and 1% zinc. Our research aims to understand the mechanical and tribological behaviors and the impact of Electrical Discharge Machining (EDM) process parameters on AZ31 magnesium alloy. We need to modify these properties for various applications. Many industrial researchers have studied the machinability of magnesium alloys using EDM. We conducted wear tests on AZ31 alloy reinforced with both B4C and MoS2 in altered quantities using a pin-on-disc setup. The outcome displays that the wear resistance of these composites is considerably better matched to other magnesium matrix composites (MMCs). We also measured various densities of the hybrid composite, including apparent density, green density, and sintered density, which were found to be 0.839, 1.495, and 1.504 g/cm3, respectively—better than other composites. In addition, the hybrid composite exhibited a substantial increase in micro hardness, reaching 22.012 HV, indicating improved wear resistance of the material. Comparatively, low density, minimum wear profile, and maximum hardness were recorded for the sample of AZ31 + 5%MoS2 + 5%B4C. The influence of EDM parameters was discussed.

Early detection of Alzheimer’s disease (AD) is essential for timely clinical intervention, yet remains challenging due to subtle neural changes in its initial stages. This study proposes a non-invasive EEG-based framework that integrates multiscale signal processing, dynamic brain connectivity analysis, nonlinear feature learning, and ensemble classification for improved diagnosis. Empirical Wavelet Transform (EWT) is employed to decompose nonstationary EEG signals into informative frequency subbands. Dynamic Functional Connectivity Graph Fusion (DFCG) is then used to model time-varying inter-channel synchronization patterns across multiple EEG rhythms. Discriminative representations are extracted using Nonlinear Manifold Statistical Feature Encoding (NMSFE), which preserves the intrinsic geometric structure of connectivity features. Finally, a Bio-Inspired Deep Ensemble Neural Network (BIDENN) performs classification by adaptively combining multiple deep learners to enhance robustness and generalization. Experimental evaluation on publicly available EEG datasets, using cross-validation and external validation protocols, demonstrates consistent performance with an accuracy of 96.2%, precision of 95.8%, recall of 96.5%, and F1-score of 96.1%. The results, supported by confusion matrix and ROC analysis, indicate that the proposed approach provides a reliable and efficient tool for early-stage Alzheimer’s disease detection.