Nanofibers (NFs) are emerging as promising materials for energy storage applications. Their nanoscale architecture enhances charge transport, making them ideal for batteries, supercapacitors and fuel cells. By incorporating conductive polymers, carbon and metal oxides, nanofibers can achieve high electrical conductivity and specific capacities for high-performance energy devices. In lithium-ion batteries (LIBs), NF-based anodes and cathodes enabled high energy density, improved cycling stability and faster charging. NF electrodes in supercapacitors increased capacitance and enhanced charge-discharge rates due to efficient electron and ion pathways. In fuel cells, NF catalysts provided a large active surface, hence promoting efficient redox reactions. However, challenges namely cost, scalability and long-term stability needs to be addressed for commercial viability. In conclusion, NFs hold great potential in advancing next-generation energy technologies due to their functional benefits.

High-entropy alloys (HEAs) are revolutionary materials containing five or more primary elements in near-equiatomic amounts to produce an alloy with high configurational entropy that stabilizes at a simple solid solution. Unlike conventional alloys, which are based on one dominant element, HEAs represent a novel advanced material that has shown great properties. These properties are the reason for scientific interest, as they have potential industrial applications due to their exceptional mechanical, thermal, and chemical properties. This paper aims to explore the different rules and principles that dictate their phase formation, including the thermodynamic and kinetic factors that are involved in the development of their phases and nanostructures. The four key effects associated with HEAs will also be explored: the high-entropy effect, severe lattice distortion effect, sluggish diffusion effect, and cocktail effect. The relationship between their unique phase formations and nanostructures and their properties will be discussed, with a view to optimizing them to prioritize certain properties for targeted applications. The mechanisms that occur and dictate these effects on the HEA for its strengthening and deformation will be analyzed to identify the benefits and trade-offs associated with modifying alloy composition and processing parameters. The fabrication methods for different types of HEAs will be reviewed along with current and emerging industrial applications of HEAs, especially in fields that require superior mechanical performance and environmental resistance. Thus, this review highlights their behavior, optimization, and potential applications as the next generation of materials.

Background: Bone marrow changes in lumbar vertebrae (BMCLVB) are critically linked to lower spinal pain and various spinal disorders, forming a key diagnostic basis. Accurate automated classification using MRI remains a developing area. Objective: This study aimed to develop a hybrid CNN-LSTM model for the automatic classification of BMCLVB from multi-sequence MRI scans. Methods: A newly collected annotated dataset consisting of magnetic resonance imaging scans from 134 patients was utilized, incorporating sagittal T1-weighted, T2-weighted, and STIR sequences. The proposed model uses CNN layers to extract the spatial features from each MRI slice and LSTM layers to capture sequential dependencies across consecutive slices. The dataset was divided into training, validation, and testing groups in a ratio of 70%:20%:10%. The experimental results show that the CNN-LSTM model achieves a classification accuracy of 90.29%, with precision, recall, and F1-score all reaching 91.07%. The proposed approach surpasses various state-of-the-art deep learning models, including CNN, VGG16, VGG19, Xception, ResNet variants, DenseNet201, InceptionResNetV2, NASNetMobile, MobileNetV2, and NASNetLarge. Results: The proposed CNN-LSTM model obtained a test classification accuracy of 90.29%. Precision, recall, and F1-score all reached 91.07%. This performance surpassed that of all benchmark models. Conclusion: The outcomes confirm that the hybrid CNN-LSTM model is highly effective for BMCLVB classification and has strong potential as a clinical decision support tool. This study successfully met its objective, showing the advantage of merged spatial and sequential feature learning for this task. Future work should emphasize extending the dataset and further optimizing the model to improve generalizability across diverse populations and imaging protocols. Keywords: Bone Marrow Changes Lumbar Vertebrae, MRI, Classification, Hybrid CNN-LSTM Model, Medical Image Analysis