Application of novel pretreatment technologies for intensification of drying performance and quality attributes of food commodities: a review

Food Science and Biotechnology - Drying is an energy-intensive process that can be reduced by the application of pretreatment prior to drying to enhance mass transfer and minimize energy...     

Multiwavelength plasmonic activity in aluminum-based 2D nanostructures for biosensing applications

Journal of Computational Electronics - Surface plasmons (SP) can be effectively tuned in the entire UV–visible–near-infrared (NIR) spectrum, bringing together a variety of optical...     

A comparative study of fourteen deep learning networks for multi skin lesion classification (MSLC) on unbalanced data

Neural Computing and Applications - Among various types of skin diseases, skin cancer is the deadliest form of the disease. This paper classifies seven types of skin diseases: Actinic keratosis and...     

Hierarchical 3D Flower-like Metal Oxides Micro/Nanostructures: Fabrication,Surface Modification,Their Crucial Role in Environmental Decontamination,Mechanistic Insights,and Future Perspectives

Hierarchical micro/nanostructures are constructed by micro-scaled objects with nanoarchitectures belonging to an interesting class of crystalline materials that has significant applications in diverse fields. Featured with a large surface-to-volume ratio, facile mass transportation, high stability against aggregation, structurally enhanced adsorption, and catalytical performances, three dimenisional (3D) hierarchical metal oxides have been considered as versatile functional materials for waste-water treatment. Due to the ineffectiveness of traditional water purification protocols for reclamation of water, lately, the use of hierarchical metal oxides has emerged as an appealing platform for the remediation of water pollution owing to their fascinating and tailorable physiochemical properties. The present review highlights various approaches to the tunable synthesis of hierarchical structures along with their surface modification strategies to enhance their efficiencies for the removal of different noxious substances. Besides, their applications for the eradication of organic and inorganic contaminants have been discussed comprehensively with their plausible mechanistic pathways. Finally, overlooked aspects in this field as well as the major roadblocks to the implementation of these metal oxide architectures for large-scale treatment of wastewater are provided here. Moreover, the potential ways to tackle these issues are also presented which may be useful for the transformation of current water treatment technologies.     

Deep learning and machine learning-based early survival predictions of glioblastoma patients using pre-operative three-dimensional brain magnetic resonance imaging modalities

To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep learning (DL) based 3D U-shaped Convolutional Neural Network inspired encoder-decoder architecture to segment the brain tumor. Further, feature extraction was performed on these segmented and raw magnetic resonance imaging (MRI) scans using a pre-trained 2D residual neural network. The dimension-reduced principal components were integrated with clinical data and the handcrafted features of tumor subregions to compare the performance of regression-based automated ML techniques. Through the proposed methodology, we achieved the mean squared error (MSE) of 87 067.328, median squared error of 30 915.66, and a SpearmanR correlation of 0.326 for survival prediction (SP) with the validation set of Multimodal Brain Tumor Segmentation 2020 dataset. These results made the MSE far better than the existing automated techniques for the same patients. Automated SP of GBM patients is a crucial topic with its relevance in clinical use. The results proved that DL-based feature extraction using 2D pre-trained networks is better than many heavily trained 3D and 2D prediction models from scratch. The ensembled approach has produced better results than single models. The most crucial feature affecting GBM patients' survival is the patient's age, as per the feature importance plots presented in this work. The most critical MRI modality for SP of GBM patients is the T2 fluid attenuated inversion recovery, as evident from the feature importance plots.