Magnetic Properties and Microstructural Modifications of Sm-Co-Hf Alloy Ribbons by B Addition

Journal of Superconductivity and Novel Magnetism - Microstructure optimization is highly desirable for improving the magnetic properties of permanent magnetic materials. To optimize the...     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.     

Annealing effect on structural, optical and electrical properties of pure and Mg doped tin oxide thin films

This study describes the effect of annealing at different temperatures (400–600 °C) on structural, optical and electrical behaviors of pure and Mg doped tin oxide thin films grown on the glass substrate by electron beam evaporation technique. The transformation of tetragonal to orthorhombic form due to annealing, introduced a change in the optical and electrical properties of pure and Mg doped tin oxide thin films. X-ray diffraction studies or analysis revealed the phase transformation and change in the crystalline size with increase in the annealing temperature. The morphology and roughness of the thin films were studied by Atomic force microscopy. Optical band gap increased with annealing temperature confirms the improvements of crystallinity. The quality of thin films transparency was investigated by UV/Vis-spectroscopy. Photoluminescence of pure and Mg doped tin oxide thin films shows two extra peaks one at 486 nm and other at 538 nm is due to the crystal defect created as a result of annealing temperature. These peaks became stronger and shifted to longer wavelength with increasing the annealing temperature. The complex plot (Nyquist plot) showed the data point laying on two semicircles and the resistance of grains and grain boundaries increases with the increase in annealing temperature for both pure and Mg doped tin oxide thin films.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

In situ trapped high-density single metal atoms within graphene: Iron-containing hybrids as representatives for efficient oxygen reduction

Nano Research - Atomically dispersed catalysts have attracted attention in energy conversion applications because their efficiency and chemoselectivity for special catalysis are superior to those...     

Bioactive-Tissue-Derived Nanocomposite Hydrogel for Permanent Arterial Embolization and Enhanced Vascular Healing

Transcatheter embolization is a minimally invasive procedure that uses embolic agents to intentionally block diseased or injured blood vessels for therapeutic purposes. Embolic agents in clinical practice are limited by recanalization, risk of non-target embolization, failure in coagulopathic patients, high cost, and toxicity. Here, a decellularized cardiac extracellular matrix (ECM)-based nanocomposite hydrogel is developed to provide superior mechanical stability, catheter injectability, retrievability, antibacterial properties, and biological activity to prevent recanalization. The embolic efficacy of the shear-thinning ECM-based hydrogel is shown in a porcine survival model of embolization in the iliac artery and the renal artery. The ECM-based hydrogel promotes arterial vessel wall remodeling and a fibroinflammatory response while undergoing significant biodegradation such that only 25% of the embolic material remains at 14 days. With its unprecedented proregenerative, antibacterial properties coupled with favorable mechanical properties, and its superior performance in anticoagulated blood, the ECM-based hydrogel has the potential to be a next-generation biofunctional embolic agent that can successfully treat a wide range of vascular diseases.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Comparisons of the Microstructure and Magnetic Properties of Anisotropic NdFeB Magnets Prepared by Hot Pressing and Spark Plasma Sintering

Journal of Superconductivity and Novel Magnetism - NdFeB hot-pressed and hot-deformed magnets were prepared by hot-pressed (HP) sintering and spark plasma sintering (SPS). The effects of sintering...     

Optical,DC and AC electrical investigations of 4-hydroxy coumarin molecule as an organic Schottky diode

The current–voltage (I–V) and optical characteristics of 4-hydroxy coumarin Schottky diode were investigated. The conventional methods related with device were used to extract the various diode parameters. From dielectric study low dielectric constant and loss was observed. From its optical study, an indirect allowed transition is shown by this compound. The optical band gap (E _g ) was found to be around 3.78 eV. The observed properties shown by this molecule give a bright opportunity to explore its application for different organic devices.