Deep learning for environmentally robust speech recognition

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy

MXene Based Nanocomposites for Recent Solar Energy Technologies

This article discusses the design and preparation of a modified MXene-based nanocomposite for increasing the power conversion efficiency and long-term stability of perovskite solar cells. The MXene family of materials among 2D nanomaterials has shown considerable promise in enhancing solar cell performance because of their remarkable surface-enhanced characteristics. Firstly, there are a variety of approaches to making MXene-reinforced composites, from solution mixing to powder metallurgy. In addition, their outstanding features, including high electrical conductivity, Young’s modulus, and distinctive shape, make them very advantageous for composite synthesis. In contrast, its excellent chemical stability, electronic conductivity, tunable band gaps, and ion intercalation make it a promising contender for various applications. Photovoltaic devices, which turn sunlight into electricity, are an exciting new area of research for sustainable power. Based on an analysis of recent articles, the hydro-thermal method has been widely used for synthesizing MXene-based nano-composites because of the easiness of fabrication and low cost. Finally, we identify new perspectives for adjusting the performance of MXene for various nanocomposites by controlling the composition of the two-dimensional transition metal MXene phase

Improved corrosion behavior of AZ31 alloy through ECAP processing

This study aims to establish the effects of equal channel angular pressing (ECAP) processing on the corrosion behavior and hardness values of the AZ31 Mg alloy. The AZ31 billets were processed through ECAP successfully at 250 °C and their microstructural evolution was studied using optical and field emission scanning electron microscopy. The corrosion resistance of the AZ31 alloy was studied before and after processing through ECAP. The homogeneity of the hardness distribution was studied using both sections cut parallel and perpendicular to the extrusion direction. ECAP processing resulted in highly deformed central regions with elon-gated grains aligned parallel to the extrusion direction, whereas the peripheral regions showed an ultra-fine-grain recrystallized structure. After processing, small ultra-fine secondary particles were found to be homogeneously dispersed alongside the grain boundaries of the α-Mg matrix. Regarding the corrosion properties, measurements showed that ECAP processing through 1-P and 2-Bc resulted in decreasing their corrosion rate to 67.7% and 78.3%, respectively, of their as-annealed counterpart’s. The corrosion resistance of the ECAPed Mg alloy increased with the number of processing passes. This was due to the refinement of the grain size of the α-Mg matrix and secondary phases till ultra-fine size, caused by the accumulation of strain during pro-cessing. On the other hand, ECAP processing through 2-Bc resulted in increasing the Vickers hardness values by 132% and 71.8% at the peripheral and central areas, respectively, compared to the as-annealed counterpart